class_docstr: Charades video loader. Construct the Charades video loader, then sample
  clips from the videos. For training and validation, a single clip is randomly
  sampled from every video with random cropping, scaling, and flipping. For
  testing, multiple clips are uniformaly sampled from every video with uniform
  cropping. For uniform cropping, we take the left, center, and right crop if
  the width is larger than height, or take top, center, and bottom crop if the
  height is larger than the width.
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docstr: Given the video index, return the list of frames, label, and video
  index if the video frames can be fetched.
  Args:
      index (int): the video index provided by the pytorch sampler.
  Returns:
      frames (tensor): the frames of sampled from the video. The dimension
          is `channel` x `num frames` x `height` x `width`.
      label (int): the label of the current video.
      index (int): the index of the video.
      time index (zero): The time index is currently not supported.
      {} extra data, currently not supported
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code: def __getitem__(self, index):
      """
      Given the video index, return the list of frames, label, and video
      index if the video frames can be fetched.
      Args:
          index (int): the video index provided by the pytorch sampler.
      Returns:
          frames (tensor): the frames of sampled from the video. The dimension
              is `channel` x `num frames` x `height` x `width`.
          label (int): the label of the current video.
          index (int): the index of the video.
          time index (zero): The time index is currently not supported.
          {} extra data, currently not supported
      """
      short_cycle_idx = None
      # When short cycle is used, input index is a tupple.
      if isinstance(index, tuple):
          index, self._num_yielded = index
          if self.cfg.MULTIGRID.SHORT_CYCLE:
              index, short_cycle_idx = index
  
      if self.mode in ["train", "val"]:
          # -1 indicates random sampling.
          spatial_sample_index = -1
          min_scale = self.cfg.DATA.TRAIN_JITTER_SCALES[0]
          max_scale = self.cfg.DATA.TRAIN_JITTER_SCALES[1]
          crop_size = self.cfg.DATA.TRAIN_CROP_SIZE
          if short_cycle_idx in [0, 1]:
              crop_size = int(
                  round(
                      self.cfg.MULTIGRID.SHORT_CYCLE_FACTORS[
                          short_cycle_idx
                      ]
                      * self.cfg.MULTIGRID.DEFAULT_S
                  )
              )
          if self.cfg.MULTIGRID.DEFAULT_S > 0:
              # Decreasing the scale is equivalent to using a larger "span"
              # in a sampling grid.
              min_scale = int(
                  round(
                      float(min_scale)
                      * crop_size
                      / self.cfg.MULTIGRID.DEFAULT_S
                  )
              )
      elif self.mode in ["test"]:
          # spatial_sample_index is in [0, 1, 2]. Corresponding to left,
          # center, or right if width is larger than height, and top, middle,
          # or bottom if height is larger than width.
          spatial_sample_index = (
              self._spatial_temporal_idx[index]
              % self.cfg.TEST.NUM_SPATIAL_CROPS
          )
          min_scale, max_scale, crop_size = [
              self.cfg.DATA.TEST_CROP_SIZE
          ] * 3
          # The testing is deterministic and no jitter should be performed.
          # min_scale, max_scale, and crop_size are expect to be the same.
          assert len({min_scale, max_scale, crop_size}) == 1
      else:
          raise NotImplementedError(
              "Does not support {} mode".format(self.mode)
          )
  
      seq = self.get_seq_frames(index)
      frames = torch.as_tensor(
          utils.retry_load_images(
              [self._path_to_videos[index][frame] for frame in seq],
              self._num_retries,
          )
      )
  
      label = utils.aggregate_labels(
          [self._labels[index][i] for i in range(seq[0], seq[-1] + 1)]
      )
      label = torch.as_tensor(
          utils.as_binary_vector(label, self.cfg.MODEL.NUM_CLASSES)
      )
  
      # Perform color normalization.
      frames = utils.tensor_normalize(
          frames, self.cfg.DATA.MEAN, self.cfg.DATA.STD
      )
      # T H W C -> C T H W.
      frames = frames.permute(3, 0, 1, 2)
      # Perform data augmentation.
      frames = utils.spatial_sampling(
          frames,
          spatial_idx=spatial_sample_index,
          min_scale=min_scale,
          max_scale=max_scale,
          crop_size=crop_size,
          random_horizontal_flip=self.cfg.DATA.RANDOM_FLIP,
          inverse_uniform_sampling=self.cfg.DATA.INV_UNIFORM_SAMPLE,
      )
      frames = utils.pack_pathway_output(self.cfg, frames)
      return frames, label, index, 0, {}
  
file_path: SlowFast/slowfast/datasets/charades.py
imports: import os
  import random
  from itertools import chain
  import torch
  import torch.utils.data
  import slowfast.utils.logging
  from slowfast.utils.env import pathmgr
  from .None import utils
  from .build import DATASET_REGISTRY
label_desc: Use image node_id 4 for calling the Charades obj's underlying member method code with example usage: obj.__getitem__(index) and returns: frames,  label,  index,  int,  dict