code: def aug_frame(
      cfg,
      mode,
      rand_erase,
      frames,
      spatial_sample_index,
      min_scale,
      max_scale,
      crop_size,
  ):
      """
      Perform augmentations on the given video frames, including
      random augmentation, normalization, spatial sampling and optional random
      erasing.
      Args:
          cfg (CfgNode): configs.
          mode (string): Options includes `train`, `val`, or `test` mode.
          rand_erase (bool): if performing random erasing.
          frames (tensor): frames of images sampled from the video. The
              dimension is `num frames` x `height` x `width` x `channel`.
          spatial_sample_index (int): if -1, perform random spatial sampling.
              If 0, 1, or 2, perform left, center, right crop if width is larger
               thanheight, and perform top, center, buttom crop if height is larger
              than width.
          min_scale (int): the minimal size of scaling.
          max_scale (int): the maximal size of scaling.
          crop_size (int): the size of height and width used to crop the
              frames.
      Returns:
          frames (tensor): spatially sampled frames.
      """
      if cfg.AUG.AA_TYPE:
          aug_transform = create_random_augment(
              input_size=(frames.size(1), frames.size(2)),
              auto_augment=cfg.AUG.AA_TYPE,
              interpolation=cfg.AUG.INTERPOLATION,
          )
          # T H W C -> T C H W.
          frames = frames.permute(0, 3, 1, 2)
          list_img = _frame_to_list_img(frames)
          list_img = aug_transform(list_img)
          frames = _list_img_to_frames(list_img)
          frames = frames.permute(0, 2, 3, 1)
  
      frames = tensor_normalize(frames, cfg.DATA.MEAN, cfg.DATA.STD)
      # T H W C -> C T H W.
      frames = frames.permute(3, 0, 1, 2)
      # Perform data augmentation.
      scl, asp = (
          cfg.DATA.TRAIN_JITTER_SCALES_RELATIVE,
          cfg.DATA.TRAIN_JITTER_ASPECT_RELATIVE,
      )
      relative_scales = (
          None if (mode not in ["train"] or len(scl) == 0) else scl
      )
      relative_aspect = (
          None if (mode not in ["train"] or len(asp) == 0) else asp
      )
      frames = spatial_sampling(
          frames,
          spatial_idx=spatial_sample_index,
          min_scale=min_scale,
          max_scale=max_scale,
          crop_size=crop_size,
          random_horizontal_flip=cfg.DATA.RANDOM_FLIP,
          inverse_uniform_sampling=cfg.DATA.INV_UNIFORM_SAMPLE,
          aspect_ratio=relative_aspect,
          scale=relative_scales,
          motion_shift=cfg.DATA.TRAIN_JITTER_MOTION_SHIFT
          if mode in ["train"]
          else False,
      )
  
      if rand_erase:
          erase_transform = RandomErasing(
              cfg.AUG.RE_PROB,
              mode=cfg.AUG.RE_MODE,
              max_count=cfg.AUG.RE_COUNT,
              num_splits=cfg.AUG.RE_COUNT,
              device="cpu",
          )
          frames = frames.permute(1, 0, 2, 3)
          frames = erase_transform(frames)
          frames = frames.permute(1, 0, 2, 3)
  
      return frames
  
imports: import logging
  import numpy
  import os
  import random
  import time
  from collections import defaultdict
  import cv2
  import torch
  from torch.utils.data.distributed import DistributedSampler
  from torchvision import transforms
  from slowfast.utils.env import pathmgr
  from .None import transform
  from .random_erasing import RandomErasing
  from .transform import create_random_augment
label_desc: Use image node_id 14 for calling a global function with example usage: aug_frame(cfg, mode, rand_erase, frames, spatial_sample_index, min_scale, max_scale, crop_size) and returns: frames