code: def _equalize(video: torch.Tensor, **kwargs) -> torch.Tensor:
      """
      Equalize the histogram of a video by applying a non-linear mapping to the input in
      order to create a uniform distribution of grayscale values in the output.
  
      Args:
          video (torch.Tensor): Video tensor with shape (T, C, H, W).
      """
      if video.dtype != torch.uint8:
          video_type = video.dtype
          video = (video * 255).to(torch.uint8)
          return (
              torchvision.transforms.functional.equalize(video) / 255
          ).to(video_type)
      return torchvision.transforms.functional.equalize(video)
  
imports: from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  import torchvision
  from torch import Tensor
  from torchvision.transforms.functional import InterpolationMode
label_desc: Use image node_id 3 for calling a global function with example usage: _equalize(video) and returns: torchvision, unknown