code: def __init__(self, config, *args, **kwargs):
      transform_params = config.transforms
      assert OmegaConf.is_dict(transform_params) or OmegaConf.is_list(
          transform_params
      )
      if OmegaConf.is_dict(transform_params):
          transform_params = [transform_params]
      pytorchvideo_spec = importlib.util.find_spec("pytorchvideo")
      assert (
          pytorchvideo_spec is not None
      ), "Must have pytorchvideo installed to use VideoTransforms"
  
      transforms_list = []
  
      for param in transform_params:
          if OmegaConf.is_dict(param):
              # This will throw config error if missing
              transform_type = param.type
              transform_param = param.get(
                  "params", OmegaConf.create({})
              )
          else:
              assert isinstance(param, str), (
                  "Each transform should either be str or dict containing "
                  "type and params"
              )
              transform_type = param
              transform_param = OmegaConf.create([])
  
          transforms_list.append(
              self.get_transform_object(transform_type, transform_param)
          )
  
      self.transform = img_transforms.Compose(transforms_list)
  
imports: import collections
  import importlib
  import logging
  import random
  import mmf.datasets.processors.functional
  import torch
  from mmf.common.registry import registry
  from mmf.datasets.processors import BaseProcessor
  from omegaconf import OmegaConf
  from torchvision import transforms
label_desc: Use image node_id 1 to create a new VideoTransforms object from inherited base classes: BaseProcessor with example: obj = VideoTransforms(config)