Added more PyTorch wrapper nodes.

modules/pytorch_wrapper/pt_std.py

@@ -0,0 +1,68 @@
+from typing import Any, Dict
+import torch
+from .utils import str_to_dim
+
+
+class PtStd:
+    """
+    Computes the standard deviation of a PyTorch tensor along the specified dimension(s).
+
+    Specify 1 to use Bessel's correction (N-1) for sample standard deviation.
+    Specify 0 to compute population standard deviation.
+
+    Specify the dimension(s) in the `dim` field using an integer, a list, or a tuple, as shown below:
+    ```
+    0, [0], or (1, 2)
+    ```
+    category: PyTorch wrapper - Reduction operation & Summary statistics
+    """
+    
+    @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", {}),
+                "correction": ("INT", {"default": 1, "min": 0, "max": 1}),
+                "dim": ("STRING", {"multiline": True}),
+                "keepdim": ("BOOLEAN", {"default": False})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("TENSOR",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, tens: torch.Tensor, dim: str, correction: int, keepdim: bool) -> tuple:
+        """
+        Computes the standard deviation along the specified dimension(s).
+
+        Args:
+            tens (torch.Tensor): The input tensor.
+            dim (str): A string representation of the dimension(s) along which to compute the standard deviation,
+                       e.g., "0", "[0]", or "(1, 2)".
+            correction (int): 0 for population standard deviation, 1 for sample standard deviation (Bessel's correction).
+            keepdim (bool): Whether to retain reduced dimensions.
+
+        Returns:
+            tuple: A tuple containing the standard deviation tensor.
+        """
+        dim = str_to_dim(dim)
+
+        # Ensure correction is valid (0 or 1)
+        if correction not in (0, 1):
+            raise ValueError(f"Invalid correction value: {correction}. Must be 0 (population) or 1 (sample).")
+
+        # Ensure keepdim is a boolean
+        if not isinstance(keepdim, bool):
+            raise TypeError(f"Invalid type for keepdim: {keepdim}. Must be a boolean.")
+
+        # Compute the standard deviation along the specified dimension(s)
+        tens = torch.std(tens, dim=dim, correction=correction, keepdim=keepdim)
+
+        return (tens,)