code: def build_transforms(
      transforms_config: Iterable[Mapping[str, Any]]
  ) -> Compose:
      """
      A utility function to build data transforsm from a list of Hydra/Omega Conf
      objects. This utility method is called by
      `pytorchvideo_trainer.datamodule.PyTorchVideoDataModule` class to build a
      sequence of transforms applied during each phase(train, val and test).
  
      Uses torchvision.transforms.Compose to build a seuquence of transforms.
  
      Examples of config objects used by this method can be found in,
      `pytorchvide_trainer/conf/datamodule/transforms/`
  
      Args:
          transforms_config: A list of hydra config objects wherein, each element
          represents config associated with a single transforms.
  
          An example of this would be,
          ```
          - _target_: pytorchvideo.transforms.ApplyTransformToKey
              transform:
              - _target_: pytorchvideo.transforms.UniformTemporalSubsample
                  num_samples: 16
              - _target_: pytorchvideo.transforms.Div255
              - _target_: pytorchvideo.transforms.Normalize
                  mean: [0.45, 0.45, 0.45]
                  std: [0.225, 0.225, 0.225]
              - _target_: pytorchvideo.transforms.ShortSideScale
                  size: 224
              key: video
          - _target_: pytorchvideo.transforms.UniformCropVideo
              size: 224
          - _target_: pytorchvideo.transforms.RemoveKey
              key: audio
          ```
      """
      transform_list = [
          build_single_transform(config) for config in transforms_config
      ]
      transform = Compose(transform_list)
      return transform
  
imports: import random
  from typing import Any, Callable, Dict, Iterable, List, Mapping, Sequence
  import hydra
  import torch
  import torchvision
  from PIL import Image, ImageFilter
  from pytorchvideo.transforms import MixVideo
  from torchvision.transforms import Compose
label_desc: Use image node_id 1 for calling a global function with example usage: build_transforms(transforms_config) and returns: transform