Added various PyTorch wrapper nodes to generate tensors.

modules/pytorch_wrapper/pt_view.py

@@ -0,0 +1,62 @@
+from typing import Any, Dict
+import torch
+import ast
+
+
+class PtView:
+    """
+    Reshapes a PyTorch tensor into a specified shape using `torch.view()`.
+    The total number of elements must remain unchanged.
+
+    For example, if a tensor has shape (2, 3, 4), and `new_shape="[6, 4]"`,
+    the output will have shape (6, 4).
+
+    Use -1 to automatically infer a dimension, e.g.:
+    ```
+    [2, -1]
+    ```
+    category: PyTorch wrapper - Transform
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "tens": ("TENSOR", {}),
+                "new_shape": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("TENSOR",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, tens: torch.Tensor, new_shape: str) -> tuple:
+        """
+        Reshapes the input tensor based on the specified shape.
+
+        Args:
+            tens (torch.Tensor): The input tensor.
+            new_shape (str): A string representation of the desired shape,
+                             e.g., "[2, -1]" or "(6, 4)".
+
+        Returns:
+            tuple: A tuple containing the reshaped PyTorch tensor.
+        """
+        # Convert string to a tuple of integers
+        shape_order = tuple(ast.literal_eval(new_shape))
+
+        # Ensure the parsed shape is valid
+        if not isinstance(shape_order, tuple) or not all(isinstance(i, int) for i in shape_order):
+            raise ValueError(f"Invalid format for new_shape: {new_shape}. Must be a tuple or list of integers.")
+
+        # Apply view operation
+        tens = tens.view(*shape_order)
+
+        return (tens,)