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Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_gru_cell_ops.h

@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API quantized_gru_cell {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Scalar &, const at::Scalar &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::quantized_gru_cell";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "quantized_gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor";
+  static at::Tensor call(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_lstm_cell_ops.h

@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API quantized_lstm_cell {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, at::TensorList, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Scalar &, const at::Scalar &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::quantized_lstm_cell";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "quantized_lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_max_pool1d_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & quantized_max_pool1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false);
+TORCH_API at::Tensor & quantized_max_pool1d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_max_pool1d_native.h

@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & quantized_max_pool1d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out);
+TORCH_API at::Tensor quantized_max_pool1d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false);
+} // namespace native
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_max_pool2d_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & quantized_max_pool2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false);
+TORCH_API at::Tensor & quantized_max_pool2d_outf(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_max_pool3d_ops.h

@@ -0,0 +1,40 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API quantized_max_pool3d {
+  using schema = at::Tensor (const at::Tensor &, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::quantized_max_pool3d";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "quantized_max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode);
+};
+
+struct TORCH_API quantized_max_pool3d_out {
+  using schema = at::Tensor & (const at::Tensor &, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, at::IntArrayRef, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::quantized_max_pool3d";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "quantized_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_rnn_relu_cell.h

@@ -0,0 +1,31 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/quantized_rnn_relu_cell_ops.h>
+
+namespace at {
+
+
+// aten::quantized_rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor
+inline at::Tensor quantized_rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) {
+    return at::_ops::quantized_rnn_relu_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
+}
+
+}

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_rnn_relu_cell_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor quantized_rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+
+} // namespace compositeimplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_rnn_relu_cell_ops.h

@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API quantized_rnn_relu_cell {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Scalar &, const at::Scalar &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::quantized_rnn_relu_cell";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "quantized_rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor";
+  static at::Tensor call(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_rnn_tanh_cell.h

@@ -0,0 +1,31 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/quantized_rnn_tanh_cell_ops.h>
+
+namespace at {
+
+
+// aten::quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor
+inline at::Tensor quantized_rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) {
+    return at::_ops::quantized_rnn_tanh_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
+}
+
+}

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_rnn_tanh_cell_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor quantized_rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+
+} // namespace compositeimplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_rnn_tanh_cell_native.h

@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor quantized_rnn_tanh_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+} // namespace native
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/quantized_rnn_tanh_cell_ops.h

@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API quantized_rnn_tanh_cell {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Scalar &, const at::Scalar &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::quantized_rnn_tanh_cell";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor";
+  static at::Tensor call(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rad2deg.h

@@ -0,0 +1,45 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/rad2deg_ops.h>
+
+namespace at {
+
+
+// aten::rad2deg(Tensor self) -> Tensor
+inline at::Tensor rad2deg(const at::Tensor & self) {
+    return at::_ops::rad2deg::call(self);
+}
+
+// aten::rad2deg_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & rad2deg_(at::Tensor & self) {
+    return at::_ops::rad2deg_::call(self);
+}
+
+// aten::rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rad2deg_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::rad2deg_out::call(self, out);
+}
+// aten::rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rad2deg_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::rad2deg_out::call(self, out);
+}
+
+}

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rad2deg_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor rad2deg(const at::Tensor & self);
+TORCH_API at::Tensor & rad2deg_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & rad2deg_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & rad2deg_(at::Tensor & self);
+
+} // namespace compositeexplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rad2deg_native.h

@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor rad2deg(const at::Tensor & self);
+TORCH_API at::Tensor & rad2deg_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & rad2deg_(at::Tensor & self);
+TORCH_API at::Tensor rad2deg_sparse(const at::Tensor & self);
+TORCH_API at::Tensor & rad2deg_sparse_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & rad2deg_sparse_(at::Tensor & self);
+TORCH_API at::Tensor rad2deg_sparse_csr(const at::Tensor & self);
+TORCH_API at::Tensor & rad2deg_sparse_csr_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & rad2deg_sparse_csr_(at::Tensor & self);
+} // namespace native
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rad2deg_ops.h

@@ -0,0 +1,51 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API rad2deg {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rad2deg";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "rad2deg(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API rad2deg_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rad2deg_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "rad2deg_(Tensor(a!) self) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API rad2deg_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rad2deg";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rand.h

@@ -0,0 +1,378 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/rand_ops.h>
+
+namespace at {
+
+
+// aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::rand_names::call(c10::fromIntArrayRefSlow(size), names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::rand_names::call(c10::fromIntArrayRefSlow(size), names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_names::call(c10::fromIntArrayRefSlow(size), names, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_names::call(c10::fromIntArrayRefSlow(size), names, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::rand_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::rand_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_names::call(size, names, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_names::call(size, names, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::rand_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::rand_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::rand_generator_with_names::call(size, generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::rand_generator_with_names::call(size, generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_generator_with_names::call(size, generator, names, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_generator_with_names::call(size, generator, names, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand(at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::rand::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor rand(at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::rand::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand_symint(c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::rand::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor rand(c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::rand::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand::call(size, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand::call(size, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
+    return at::_ops::rand_generator::call(c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
+    return at::_ops::rand_generator::call(c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_generator::call(c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_generator::call(c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
+    return at::_ops::rand_generator::call(size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
+    return at::_ops::rand_generator::call(size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_generator::call(size, generator, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor rand(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::rand_generator::call(size, generator, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::rand_out::call(c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::rand_out::call(c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::rand_out::call(c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & rand_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::rand_out::call(c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size) {
+    return at::_ops::rand_out::call(size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & rand_out(at::Tensor & out, c10::SymIntArrayRef size) {
+    return at::_ops::rand_out::call(size, out);
+  }
+}
+
+// aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::rand_out::call(size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & rand_outf(c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::rand_out::call(size, out);
+  }
+}
+
+// aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::rand_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::rand_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
+  }
+}
+
+// aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::rand_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::rand_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
+  }
+}
+
+// aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::rand_generator_out::call(size, generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & rand_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::rand_generator_out::call(size, generator, out);
+  }
+}
+
+// aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::rand_generator_out::call(size, generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & rand_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::rand_generator_out::call(size, generator, out);
+  }
+}
+
+// aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
+    return at::_ops::rand_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
+    return at::_ops::rand_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
+  }
+}
+
+// aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::rand_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::rand_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
+  }
+}
+
+// aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names) {
+    return at::_ops::rand_names_out::call(size, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & rand_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names) {
+    return at::_ops::rand_names_out::call(size, names, out);
+  }
+}
+
+// aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::rand_names_out::call(size, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & rand_outf(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::rand_names_out::call(size, names, out);
+  }
+}
+
+// aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
+    return at::_ops::rand_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
+    return at::_ops::rand_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
+  }
+}
+
+// aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::rand_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::rand_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
+  }
+}
+
+// aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
+    return at::_ops::rand_generator_with_names_out::call(size, generator, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & rand_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
+    return at::_ops::rand_generator_with_names_out::call(size, generator, names, out);
+  }
+}
+
+// aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::rand_generator_with_names_out::call(size, generator, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & rand_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::rand_generator_with_names_out::call(size, generator, names, out);
+  }
+}
+
+}

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rand_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,50 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={});
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={});
+TORCH_API at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names);
+TORCH_API at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names);
+TORCH_API at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={});
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={});
+TORCH_API at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names);
+TORCH_API at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names);
+TORCH_API at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor rand(at::IntArrayRef size, at::TensorOptions options={});
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor rand_symint(c10::SymIntArrayRef size, at::TensorOptions options={});
+TORCH_API at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size);
+TORCH_API at::Tensor & rand_outf(at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size);
+TORCH_API at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={});
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={});
+TORCH_API at::Tensor rand_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+
+} // namespace compositeexplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rand_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor & rand_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator);
+TORCH_API at::Tensor & rand_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & rand_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator);
+TORCH_API at::Tensor & rand_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rand_like.h

@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/rand_like_ops.h>
+
+namespace at {
+
+
+// aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor rand_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::rand_like::call(self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+}
+// aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor rand_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::rand_like::call(self, dtype, layout, device, pin_memory, memory_format);
+}
+
+// aten::rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::rand_like_out::call(self, memory_format, out);
+}
+// aten::rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rand_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::rand_like_out::call(self, memory_format, out);
+}
+
+}

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rand_like_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor rand_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor rand_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor & rand_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & rand_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rand_like_native.h

@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor rand_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & rand_like_out(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+} // namespace native
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rand_like_ops.h

@@ -0,0 +1,40 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API rand_like {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rand_like";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API rand_like_out {
+  using schema = at::Tensor & (const at::Tensor &, ::std::optional<at::MemoryFormat>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rand_like";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rand_native.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & rand_names_out_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & rand_generator_with_names_out_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & rand_out(at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & rand_out(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor rand(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+} // namespace native
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/rand_ops.h

@@ -0,0 +1,106 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API rand_names {
+  using schema = at::Tensor (c10::SymIntArrayRef, ::std::optional<at::DimnameList>, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rand";
+  static constexpr const char* overload_name = "names";
+  static constexpr const char* schema_str = "rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API rand_generator_with_names {
+  using schema = at::Tensor (c10::SymIntArrayRef, ::std::optional<at::Generator>, ::std::optional<at::DimnameList>, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rand";
+  static constexpr const char* overload_name = "generator_with_names";
+  static constexpr const char* schema_str = "rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API rand {
+  using schema = at::Tensor (c10::SymIntArrayRef, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rand";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API rand_generator {
+  using schema = at::Tensor (c10::SymIntArrayRef, ::std::optional<at::Generator>, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rand";
+  static constexpr const char* overload_name = "generator";
+  static constexpr const char* schema_str = "rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API rand_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rand";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymIntArrayRef size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out);
+};
+
+struct TORCH_API rand_generator_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, ::std::optional<at::Generator>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rand";
+  static constexpr const char* overload_name = "generator_out";
+  static constexpr const char* schema_str = "rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+};
+
+struct TORCH_API rand_names_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, ::std::optional<at::DimnameList>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rand";
+  static constexpr const char* overload_name = "names_out";
+  static constexpr const char* schema_str = "rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+};
+
+struct TORCH_API rand_generator_with_names_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, ::std::optional<at::Generator>, ::std::optional<at::DimnameList>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::rand";
+  static constexpr const char* overload_name = "generator_with_names_out";
+  static constexpr const char* schema_str = "rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randint.h

@@ -0,0 +1,378 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/randint_ops.h>
+
+namespace at {
+
+
+// aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint(int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint::call(high, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint(int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint::call(high, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint::call(high, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint::call(high, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint::call(high, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint(c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint::call(high, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint::call(high, size, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint::call(high, size, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_generator::call(high, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_generator::call(high, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_generator::call(high, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_generator::call(high, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_generator::call(high, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_generator::call(high, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_generator::call(high, size, generator, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_generator::call(high, size, generator, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_low::call(low, high, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_low::call(low, high, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_low::call(low, high, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_low::call(low, high, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_low::call(low, high, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_low::call(low, high, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_low::call(low, high, size, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_low::call(low, high, size, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_low_generator::call(low, high, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_low_generator::call(low, high, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_low_generator::call(low, high, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_low_generator::call(low, high, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_low_generator::call(low, high, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong) {
+    return at::_ops::randint_low_generator::call(low, high, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_low_generator::call(low, high, size, generator, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randint_low_generator::call(low, high, size, generator, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_out(at::Tensor & out, int64_t high, at::IntArrayRef size) {
+    return at::_ops::randint_out::call(high, c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_out(at::Tensor & out, int64_t high, at::IntArrayRef size) {
+    return at::_ops::randint_out::call(high, c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_outf(int64_t high, at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::randint_out::call(high, c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_outf(int64_t high, at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::randint_out::call(high, c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size) {
+    return at::_ops::randint_out::call(high, size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_out(at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size) {
+    return at::_ops::randint_out::call(high, size, out);
+  }
+}
+
+// aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_symint_outf(c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::randint_out::call(high, size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_outf(c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::randint_out::call(high, size, out);
+  }
+}
+
+// aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_out(at::Tensor & out, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randint_generator_out::call(high, c10::fromIntArrayRefSlow(size), generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_out(at::Tensor & out, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randint_generator_out::call(high, c10::fromIntArrayRefSlow(size), generator, out);
+  }
+}
+
+// aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_outf(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randint_generator_out::call(high, c10::fromIntArrayRefSlow(size), generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_outf(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randint_generator_out::call(high, c10::fromIntArrayRefSlow(size), generator, out);
+  }
+}
+
+// aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randint_generator_out::call(high, size, generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_out(at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randint_generator_out::call(high, size, generator, out);
+  }
+}
+
+// aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_symint_outf(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randint_generator_out::call(high, size, generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_outf(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randint_generator_out::call(high, size, generator, out);
+  }
+}
+
+// aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_out(at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size) {
+    return at::_ops::randint_low_out::call(low, high, c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_out(at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size) {
+    return at::_ops::randint_low_out::call(low, high, c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_outf(int64_t low, int64_t high, at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::randint_low_out::call(low, high, c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_outf(int64_t low, int64_t high, at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::randint_low_out::call(low, high, c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size) {
+    return at::_ops::randint_low_out::call(low, high, size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_out(at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size) {
+    return at::_ops::randint_low_out::call(low, high, size, out);
+  }
+}
+
+// aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_symint_outf(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::randint_low_out::call(low, high, size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_outf(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::randint_low_out::call(low, high, size, out);
+  }
+}
+
+// aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_out(at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randint_low_generator_out::call(low, high, c10::fromIntArrayRefSlow(size), generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_out(at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randint_low_generator_out::call(low, high, c10::fromIntArrayRefSlow(size), generator, out);
+  }
+}
+
+// aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_outf(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randint_low_generator_out::call(low, high, c10::fromIntArrayRefSlow(size), generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_outf(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randint_low_generator_out::call(low, high, c10::fromIntArrayRefSlow(size), generator, out);
+  }
+}
+
+// aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randint_low_generator_out::call(low, high, size, generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_out(at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randint_low_generator_out::call(low, high, size, generator, out);
+  }
+}
+
+// aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_symint_outf(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randint_low_generator_out::call(low, high, size, generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_outf(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randint_low_generator_out::call(low, high, size, generator, out);
+  }
+}
+
+}

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randint_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,54 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor randint(int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & randint_out(at::Tensor & out, int64_t high, at::IntArrayRef size);
+TORCH_API at::Tensor & randint_outf(int64_t high, at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size);
+TORCH_API at::Tensor & randint_symint_outf(c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor randint_symint(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & randint_out(at::Tensor & out, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator);
+TORCH_API at::Tensor & randint_outf(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator);
+TORCH_API at::Tensor & randint_symint_outf(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & randint_out(at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size);
+TORCH_API at::Tensor & randint_outf(int64_t low, int64_t high, at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size);
+TORCH_API at::Tensor & randint_symint_outf(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor randint_symint(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & randint_out(at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator);
+TORCH_API at::Tensor & randint_outf(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & randint_symint_out(at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator);
+TORCH_API at::Tensor & randint_symint_outf(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randint_like.h

@@ -0,0 +1,220 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/randint_like_ops.h>
+
+namespace at {
+
+
+// aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randint_like(const at::Tensor & self, int64_t high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like::call(self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint_like(const at::Tensor & self, int64_t high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like::call(self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+  }
+}
+
+// aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randint_like(const at::Tensor & self, int64_t high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::randint_like::call(self, high, dtype, layout, device, pin_memory, memory_format);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint_like(const at::Tensor & self, int64_t high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::randint_like::call(self, high, dtype, layout, device, pin_memory, memory_format);
+  }
+}
+
+// aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like::call(self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint_like(const at::Tensor & self, c10::SymInt high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like::call(self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+  }
+}
+
+// aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::randint_like::call(self, high, dtype, layout, device, pin_memory, memory_format);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint_like(const at::Tensor & self, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::randint_like::call(self, high, dtype, layout, device, pin_memory, memory_format);
+  }
+}
+
+// aten::randint_like.Tensor(Tensor self, Tensor high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randint_like(const at::Tensor & self, const at::Tensor & high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_Tensor::call(self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+}
+// aten::randint_like.Tensor(Tensor self, Tensor high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randint_like(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::randint_like_Tensor::call(self, high, dtype, layout, device, pin_memory, memory_format);
+}
+
+// aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_low_dtype::call(self, low, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_low_dtype::call(self, low, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+  }
+}
+
+// aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::randint_like_low_dtype::call(self, low, high, dtype, layout, device, pin_memory, memory_format);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::randint_like_low_dtype::call(self, low, high, dtype, layout, device, pin_memory, memory_format);
+  }
+}
+
+// aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt low, c10::SymInt high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_low_dtype::call(self, low, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint_like(const at::Tensor & self, c10::SymInt low, c10::SymInt high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_low_dtype::call(self, low, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+  }
+}
+
+// aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::randint_like_low_dtype::call(self, low, high, dtype, layout, device, pin_memory, memory_format);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randint_like(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::randint_like_low_dtype::call(self, low, high, dtype, layout, device, pin_memory, memory_format);
+  }
+}
+
+// aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, int64_t high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_out::call(self, high, memory_format, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, int64_t high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_out::call(self, high, memory_format, out);
+  }
+}
+
+// aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_like_outf(const at::Tensor & self, int64_t high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::randint_like_out::call(self, high, memory_format, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_like_outf(const at::Tensor & self, int64_t high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::randint_like_out::call(self, high, memory_format, out);
+  }
+}
+
+// aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_like_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_out::call(self, high, memory_format, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_out::call(self, high, memory_format, out);
+  }
+}
+
+// aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_like_symint_outf(const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::randint_like_out::call(self, high, memory_format, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_like_outf(const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::randint_like_out::call(self, high, memory_format, out);
+  }
+}
+
+// aten::randint_like.Tensor_out(Tensor self, Tensor high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_Tensor_out::call(self, high, memory_format, out);
+}
+// aten::randint_like.Tensor_out(Tensor self, Tensor high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_like_outf(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::randint_like_Tensor_out::call(self, high, memory_format, out);
+}
+
+// aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
+  }
+}
+
+// aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_like_outf(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randint_like_outf(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
+  }
+}
+
+// aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_like_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
+  }
+}
+
+// aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randint_like_symint_outf(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randint_like_outf(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::randint_like_low_dtype_out::call(self, low, high, memory_format, out);
+  }
+}
+
+}

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randint_like_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,42 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor randint_like(const at::Tensor & self, int64_t high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor randint_like(const at::Tensor & self, int64_t high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, int64_t high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & randint_like_outf(const at::Tensor & self, int64_t high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+TORCH_API at::Tensor & randint_like_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & randint_like_symint_outf(const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+TORCH_API at::Tensor randint_like(const at::Tensor & self, const at::Tensor & high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor randint_like(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & randint_like_outf(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+TORCH_API at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt low, c10::SymInt high, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor randint_like_symint(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor & randint_like_out(at::Tensor & out, const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & randint_like_outf(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+TORCH_API at::Tensor & randint_like_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & randint_like_symint_outf(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randint_like_native.h

@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor randint_like(const at::Tensor & self, int64_t high, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & randint_like_out_symint(const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+TORCH_API at::Tensor randint_like(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & randint_like_Tensor_out(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+TORCH_API at::Tensor randint_like(const at::Tensor & self, int64_t low, int64_t high, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & randint_like_low_dtype_out_symint(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+} // namespace native
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randint_like_ops.h

@@ -0,0 +1,84 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API randint_like {
+  using schema = at::Tensor (const at::Tensor &, c10::SymInt, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint_like";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API randint_like_Tensor {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint_like";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "randint_like.Tensor(Tensor self, Tensor high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API randint_like_low_dtype {
+  using schema = at::Tensor (const at::Tensor &, c10::SymInt, c10::SymInt, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint_like";
+  static constexpr const char* overload_name = "low_dtype";
+  static constexpr const char* schema_str = "randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API randint_like_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymInt, ::std::optional<at::MemoryFormat>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint_like";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+};
+
+struct TORCH_API randint_like_Tensor_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, ::std::optional<at::MemoryFormat>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint_like";
+  static constexpr const char* overload_name = "Tensor_out";
+  static constexpr const char* schema_str = "randint_like.Tensor_out(Tensor self, Tensor high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+};
+
+struct TORCH_API randint_like_low_dtype_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymInt, c10::SymInt, ::std::optional<at::MemoryFormat>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint_like";
+  static constexpr const char* overload_name = "low_dtype_out";
+  static constexpr const char* schema_str = "randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt low, c10::SymInt high, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randint_native.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & randint_out(int64_t high, at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor randint(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & randint_out(int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & randint_out(int64_t low, int64_t high, at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor randint(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & randint_out(int64_t low, int64_t high, at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+} // namespace native
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randint_ops.h

@@ -0,0 +1,106 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API randint {
+  using schema = at::Tensor (c10::SymInt, c10::SymIntArrayRef, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API randint_generator {
+  using schema = at::Tensor (c10::SymInt, c10::SymIntArrayRef, ::std::optional<at::Generator>, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint";
+  static constexpr const char* overload_name = "generator";
+  static constexpr const char* schema_str = "randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API randint_low {
+  using schema = at::Tensor (c10::SymInt, c10::SymInt, c10::SymIntArrayRef, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint";
+  static constexpr const char* overload_name = "low";
+  static constexpr const char* schema_str = "randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API randint_low_generator {
+  using schema = at::Tensor (c10::SymInt, c10::SymInt, c10::SymIntArrayRef, ::std::optional<at::Generator>, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint";
+  static constexpr const char* overload_name = "low_generator";
+  static constexpr const char* schema_str = "randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API randint_out {
+  using schema = at::Tensor & (c10::SymInt, c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out);
+};
+
+struct TORCH_API randint_generator_out {
+  using schema = at::Tensor & (c10::SymInt, c10::SymIntArrayRef, ::std::optional<at::Generator>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint";
+  static constexpr const char* overload_name = "generator_out";
+  static constexpr const char* schema_str = "randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+};
+
+struct TORCH_API randint_low_out {
+  using schema = at::Tensor & (c10::SymInt, c10::SymInt, c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint";
+  static constexpr const char* overload_name = "low_out";
+  static constexpr const char* schema_str = "randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out);
+};
+
+struct TORCH_API randint_low_generator_out {
+  using schema = at::Tensor & (c10::SymInt, c10::SymInt, c10::SymIntArrayRef, ::std::optional<at::Generator>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randint";
+  static constexpr const char* overload_name = "low_generator_out";
+  static constexpr const char* schema_str = "randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randn.h

@@ -0,0 +1,378 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/randn_ops.h>
+
+namespace at {
+
+
+// aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn(at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::randn::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randn(at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::randn::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn_symint(c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::randn::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randn(c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::randn::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn::call(size, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn::call(size, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
+    return at::_ops::randn_generator::call(c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
+    return at::_ops::randn_generator::call(c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_generator::call(c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_generator::call(c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
+    return at::_ops::randn_generator::call(size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={}) {
+    return at::_ops::randn_generator::call(size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_generator::call(size, generator, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_generator::call(size, generator, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::randn_names::call(c10::fromIntArrayRefSlow(size), names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::randn_names::call(c10::fromIntArrayRefSlow(size), names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_names::call(c10::fromIntArrayRefSlow(size), names, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_names::call(c10::fromIntArrayRefSlow(size), names, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::randn_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::randn_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_names::call(size, names, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_names::call(size, names, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::randn_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::randn_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_generator_with_names::call(c10::fromIntArrayRefSlow(size), generator, names, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::randn_generator_with_names::call(size, generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::randn_generator_with_names::call(size, generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_generator_with_names::call(size, generator, names, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor randn(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::randn_generator_with_names::call(size, generator, names, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::randn_out::call(c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::randn_out::call(c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::randn_out::call(c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randn_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::randn_out::call(c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size) {
+    return at::_ops::randn_out::call(size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randn_out(at::Tensor & out, c10::SymIntArrayRef size) {
+    return at::_ops::randn_out::call(size, out);
+  }
+}
+
+// aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::randn_out::call(size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randn_outf(c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::randn_out::call(size, out);
+  }
+}
+
+// aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randn_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randn_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
+  }
+}
+
+// aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randn_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randn_generator_out::call(c10::fromIntArrayRefSlow(size), generator, out);
+  }
+}
+
+// aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randn_generator_out::call(size, generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randn_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator) {
+    return at::_ops::randn_generator_out::call(size, generator, out);
+  }
+}
+
+// aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randn_generator_out::call(size, generator, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randn_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::randn_generator_out::call(size, generator, out);
+  }
+}
+
+// aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
+    return at::_ops::randn_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
+    return at::_ops::randn_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
+  }
+}
+
+// aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::randn_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::randn_names_out::call(c10::fromIntArrayRefSlow(size), names, out);
+  }
+}
+
+// aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names) {
+    return at::_ops::randn_names_out::call(size, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randn_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names) {
+    return at::_ops::randn_names_out::call(size, names, out);
+  }
+}
+
+// aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::randn_names_out::call(size, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randn_outf(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::randn_names_out::call(size, names, out);
+  }
+}
+
+// aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
+    return at::_ops::randn_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
+    return at::_ops::randn_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
+  }
+}
+
+// aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::randn_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::randn_generator_with_names_out::call(c10::fromIntArrayRefSlow(size), generator, names, out);
+  }
+}
+
+// aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
+    return at::_ops::randn_generator_with_names_out::call(size, generator, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randn_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names) {
+    return at::_ops::randn_generator_with_names_out::call(size, generator, names, out);
+  }
+}
+
+// aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::randn_generator_with_names_out::call(size, generator, names, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & randn_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::randn_generator_with_names_out::call(size, generator, names, out);
+  }
+}
+
+}

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randn_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,46 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor randn(at::IntArrayRef size, at::TensorOptions options={});
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor randn_symint(c10::SymIntArrayRef size, at::TensorOptions options={});
+TORCH_API at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={});
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::TensorOptions options={});
+TORCH_API at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={});
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={});
+TORCH_API at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names);
+TORCH_API at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names);
+TORCH_API at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={});
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::TensorOptions options={});
+TORCH_API at::Tensor randn_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names);
+TORCH_API at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names);
+TORCH_API at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randn_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,30 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size);
+TORCH_API at::Tensor & randn_outf(at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size);
+TORCH_API at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & randn_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::Generator> generator);
+TORCH_API at::Tensor & randn_outf(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & randn_symint_out(at::Tensor & out, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator);
+TORCH_API at::Tensor & randn_symint_outf(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randn_like.h

@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/randn_like_ops.h>
+
+namespace at {
+
+
+// aten::randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randn_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randn_like::call(self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+}
+// aten::randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor randn_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::randn_like::call(self, dtype, layout, device, pin_memory, memory_format);
+}
+
+// aten::randn_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::randn_like_out::call(self, memory_format, out);
+}
+// aten::randn_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & randn_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::randn_like_out::call(self, memory_format, out);
+}
+
+}

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randn_like_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor randn_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor randn_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor & randn_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & randn_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randn_like_compositeimplicitautogradnestedtensor_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautogradnestedtensor {
+
+TORCH_API at::Tensor randn_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor randn_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+
+} // namespace compositeimplicitautogradnestedtensor
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randn_like_native.h

@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor randn_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & randn_like_out(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+} // namespace native
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randn_like_ops.h

@@ -0,0 +1,40 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API randn_like {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randn_like";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API randn_like_out {
+  using schema = at::Tensor & (const at::Tensor &, ::std::optional<at::MemoryFormat>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randn_like";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "randn_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randn_native.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & randn_out(at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & randn_out(at::IntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & randn_names_out_symint(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor randn(at::IntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & randn_generator_with_names_out_symint(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out);
+} // namespace native
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/randn_ops.h

@@ -0,0 +1,106 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <string_view>
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API randn {
+  using schema = at::Tensor (c10::SymIntArrayRef, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randn";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API randn_generator {
+  using schema = at::Tensor (c10::SymIntArrayRef, ::std::optional<at::Generator>, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randn";
+  static constexpr const char* overload_name = "generator";
+  static constexpr const char* schema_str = "randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API randn_names {
+  using schema = at::Tensor (c10::SymIntArrayRef, ::std::optional<at::DimnameList>, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randn";
+  static constexpr const char* overload_name = "names";
+  static constexpr const char* schema_str = "randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API randn_generator_with_names {
+  using schema = at::Tensor (c10::SymIntArrayRef, ::std::optional<at::Generator>, ::std::optional<at::DimnameList>, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randn";
+  static constexpr const char* overload_name = "generator_with_names";
+  static constexpr const char* schema_str = "randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API randn_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randn";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymIntArrayRef size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out);
+};
+
+struct TORCH_API randn_generator_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, ::std::optional<at::Generator>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randn";
+  static constexpr const char* overload_name = "generator_out";
+  static constexpr const char* schema_str = "randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, at::Tensor & out);
+};
+
+struct TORCH_API randn_names_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, ::std::optional<at::DimnameList>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randn";
+  static constexpr const char* overload_name = "names_out";
+  static constexpr const char* schema_str = "randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+};
+
+struct TORCH_API randn_generator_with_names_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, ::std::optional<at::Generator>, ::std::optional<at::DimnameList>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::randn";
+  static constexpr const char* overload_name = "generator_with_names_out";
+  static constexpr const char* schema_str = "randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::Generator> generator, ::std::optional<at::DimnameList> names, at::Tensor & out);
+};
+
+}} // namespace at::_ops

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/random.h

@@ -0,0 +1,68 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <string_view>
+
+
+
+#include <ATen/ops/random_ops.h>
+
+namespace at {
+
+
+// aten::random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & random_out(at::Tensor & out, const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt) {
+    return at::_ops::random_from_out::call(self, from, to, generator, out);
+}
+// aten::random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & random_outf(const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::random_from_out::call(self, from, to, generator, out);
+}
+
+// aten::random.from(Tensor self, int from, int? to, *, Generator? generator=None) -> Tensor
+inline at::Tensor random(const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt) {
+    return at::_ops::random_from::call(self, from, to, generator);
+}
+
+// aten::random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & random_out(at::Tensor & out, const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt) {
+    return at::_ops::random_to_out::call(self, to, generator, out);
+}
+// aten::random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & random_outf(const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::random_to_out::call(self, to, generator, out);
+}
+
+// aten::random.to(Tensor self, int to, *, Generator? generator=None) -> Tensor
+inline at::Tensor random(const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt) {
+    return at::_ops::random_to::call(self, to, generator);
+}
+
+// aten::random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & random_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
+    return at::_ops::random_out::call(self, generator, out);
+}
+// aten::random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & random_outf(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::random_out::call(self, generator, out);
+}
+
+// aten::random(Tensor self, *, Generator? generator=None) -> Tensor
+inline at::Tensor random(const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt) {
+    return at::_ops::random::call(self, generator);
+}
+
+}

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/random_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,31 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor random(const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_out(at::Tensor & out, const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_outf(const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor random(const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_out(at::Tensor & out, const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_outf(const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor random(const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_outf(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/random_cpu_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor & random_(at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_(at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_(at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt);
+
+} // namespace cpu
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/random_cuda_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor & random_(at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_(at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_(at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt);
+
+} // namespace cuda
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/random_meta_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor & random_(at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_(at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_(at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt);
+
+} // namespace meta
+} // namespace at

Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/torch/include/ATen/ops/random_native.h

@@ -0,0 +1,32 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor random(const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_from_out(const at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & random_(at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_meta_(at::Tensor & self, int64_t from, ::std::optional<int64_t> to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor random(const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_to_out(const at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & random_(at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_meta_(at::Tensor & self, int64_t to, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor random(const at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_out(const at::Tensor & self, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & random_(at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & random_meta_(at::Tensor & self, ::std::optional<at::Generator> generator=::std::nullopt);
+} // namespace native
+} // namespace at