code: def tensor_to_images(
      name: str, tensor: torch.Tensor
  ) -> np.ndarray | None:
      """
      Return an tuple of images derived from the given tensor. The `name` value indices which key from the
      output or batch value the tensor was stored as, or is "Batch" or "Output" if these were single tensors
      instead of dictionaries. Returns a tuple of 2D images of shape HW, or 3D images of shape CHW where C is
      color channels RGB or RGBA. This allows multiple images to be created from a single tensor, ie. to show
      each channel separately.
      """
      if (
          tensor.ndim == 3
          and tensor.shape[1] > 2
          and tensor.shape[2] > 2
      ):
          return tensor.cpu().data.numpy()  # type: ignore[no-any-return]
      if (
          tensor.ndim == 4
          and tensor.shape[2] > 2
          and tensor.shape[3] > 2
      ):
          dmid = tensor.shape[1] // 2
          return tensor[:, dmid].cpu().data.numpy()  # type: ignore[no-any-return]
  
      return None
  
imports: from __future__ import annotations
  import copy
  from collections.abc import Callable, Mapping
  from enum import Enum
  from threading import RLock, Thread
  from typing import TYPE_CHECKING, Any
  import numpy
  import torch
  from monai.config import IgniteInfo
  from monai.utils.module import min_version, optional_import
label_desc: Use image node_id 3 for calling a global function with example usage: tensor_to_images(name, tensor) and returns: None, tensor, tensor