class_docstr: Transform video to video & audio tensors given an video path, corresponding to models by model name.
  The code is buiil on top of examples given by [Pytorchvideo](https://pytorchvideo.org/).
  The following model names are supported:
      - slow_r50
      - slowfast_r50
      - slowfast_r101
      - x3d_xs
      - x3d_s
      - x3d_m
  
  Args:
      cfg:
          configs including parameters
  Returns:
      A dictionary including tensors for video (and audio).
  
  Example:
      >>> from towhee.models.utils.video_transforms import VideoTransforms
      >>>
      >>> model_name = "x3d_xs"
      >>> cfg = video_configs[model_name]
      >>> cfg.update(model_name=model_name)
      >>> tsfm = VideoTransforms(cfg)
      >>> output = tsfm(video)
      >>> print(output.shape)
      torch.Size([3, 4, 182, 182])
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code: def __call__(self, video: numpy.ndarray):
      assert video.dtype == numpy.float32
      video = torch.from_numpy(video)
  
      video_data = self.tfms(video)
      return video_data
  
imports: import os
  import logging
  import numpy
  import numbers
  import torch
  from torch import nn
  from torchvision.transforms import Compose
label_desc: Use image node_id 2 for calling the VideoTransforms obj's underlying member method code with example usage: obj.__call__(video) and returns: video_data