code: def tensor_to_ndarray(
      tensor: Union[torch.Tensor, List[torch.Tensor]],
      **kwargs,
  ) -> None:
      """
      Convert a Tensor into ndarray and return the array
  
      Args:
          tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,
              saves the tensor as a grid of images by calling ``make_grid``.
          fp (string or file object): A filename or a file object
          format(Optional):  If omitted, the format to use is determined from the filename extension.
              If a file object was used instead of a filename, this parameter should always be used.
          **kwargs: Other arguments are documented in ``make_grid``.
      """
  
      grid = make_grid(tensor, **kwargs)
      # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
      ndarr = (
          grid.mul(255)
          .add_(0.5)
          .clamp_(0, 255)
          .permute(1, 2, 0)
          .to("cpu", torch.uint8)
          .numpy()
      )
  
      return ndarr
  
file_path: ImageAI/imageai/retinanet/utils.py
imports: from torchvision.io import ImageReadMode
  import torch
  from PIL import Image, ImageColor, ImageDraw, ImageFont
  from typing import List, Optional, Union, Tuple, BinaryIO
  import numpy
  import math
  import warnings
  import pathlib
label_desc: Use image node_id 7 for calling a global function with example usage: tensor_to_ndarray(tensor) and returns: ndarr