docstr: Modify video data to have dtype of uint8 and values range in [0, 255].
  Args:
      frames (array-like): 4D array of shape (T, H, W, C).
  Returns:
      frames (list of frames): list of frames in range [0, 1].
      adjusted (bool): whether the original frames need adjusted.
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code: def _adjust_frames_type(self, frames):
      """
      Modify video data to have dtype of uint8 and values range in [0, 255].
      Args:
          frames (array-like): 4D array of shape (T, H, W, C).
      Returns:
          frames (list of frames): list of frames in range [0, 1].
          adjusted (bool): whether the original frames need adjusted.
      """
      assert (
          frames is not None and len(frames) != 0
      ), "Frames does not contain any values"
      frames = np.array(frames)
      assert np.array(frames).ndim == 4, "Frames must have 4 dimensions"
      adjusted = False
      if frames.dtype in [np.float32, np.float64]:
          frames *= 255
          frames = frames.astype(np.uint8)
          adjusted = True
  
      return frames, adjusted
  
imports: import itertools
  import logging
  import numpy
  import matplotlib.pyplot
  import torch
  from detectron2.utils.visualizer import Visualizer
  import slowfast.utils.logging
  from slowfast.utils.misc import get_class_names
label_desc: Use image node_id 6 for calling the VideoVisualizer obj's underlying member method code with example usage: obj._adjust_frames_type(frames) and returns: frames,  adjusted