code: def get_transform(input_transforms_list):
      """
      Given the list of user specified transforms, return the
      torchvision.transforms.Compose() version of the transforms. Each transform
      in the composition is SSLTransformsWrapper which wraps the original
      transforms to handle multi-modal nature of input.
      """
      output_transforms = []
      for transform_config in input_transforms_list:
          transform = SSLTransformsWrapper.from_config(
              transform_config, transform_types=DEFAULT_TRANSFORM_TYPES
          )
          output_transforms.append(transform)
      return pth_transforms.Compose(output_transforms)
  
imports: from pathlib import Path
  import torchvision.transforms
  from classy_vision.generic.registry_utils import import_all_modules
  from vissl.data.ssl_transforms.ssl_transforms_wrapper import DEFAULT_TRANSFORM_TYPES, SSLTransformsWrapper
label_desc: Use image node_id 1 for calling a global function with example usage: get_transform(input_transforms_list) and returns: pth_transforms