code: def build_transform(transform_config: Dict[str, Any]) -> Callable:
      """Builds a :class:`ClassyTransform` from a config.
  
      This assumes a 'name' key in the config which is used to determine what
      transform class to instantiate. For instance, a config `{"name":
      "my_transform", "foo": "bar"}` will find a class that was registered as
      "my_transform" (see :func:`register_transform`) and call .from_config on
      it.
  
      In addition to transforms registered with :func:`register_transform`, we
      also support instantiating transforms available in the
      `torchvision.transforms <https://pytorch.org/docs/stable/torchvision/
      transforms.html>`_ module. Any keys in the config will get expanded
      to parameters of the transform constructor. For instance, the following
      call will instantiate a :class:`torchvision.transforms.CenterCrop`:
  
      .. code-block:: python
  
        build_transform({"name": "CenterCrop", "size": 224})
      """
      assert (
          "name" in transform_config
      ), f"name not provided for transform: {transform_config}"
      name = transform_config["name"]
  
      transform_args = {
          k: v for k, v in transform_config.items() if k != "name"
      }
  
      if name in TRANSFORM_REGISTRY:
          transform = TRANSFORM_REGISTRY[name].from_config(
              transform_args
          )
      else:
          # the name should be available in torchvision.transforms
          # if users specify the torchvision transform name in snake case,
          # we need to convert it to title case.
  
          if not (
              hasattr(transforms, name) or (name in TRANSFORM_VIDEO)
          ):
              name = name.title().replace("_", "")
          assert hasattr(transforms, name) or (
              name in TRANSFORM_VIDEO
          ), (
              f"{name} isn't a registered tranform"
              ", nor is it available in torchvision.transforms"
          )
          if hasattr(transforms, name):
              transform = getattr(transforms, name)(**transform_args)
          else:
              import torchvision.transforms._transforms_video as transforms_video
  
              transform = getattr(transforms_video, name)(
                  **transform_args
              )
  
      log_class_usage("Transform", transform.__class__)
      return transform
  
file_path: ClassyVision/classy_vision/dataset/transforms/__init__.py
imports: import copy
  import traceback
  from pathlib import Path
  from typing import Any, Callable, Dict, List
  import torchvision.transforms
  from classy_vision.generic.registry_utils import import_all_modules
  from classy_vision.generic.util import log_class_usage
  from .classy_transform import ClassyTransform
  from .lighting_transform import LightingTransform
  from .util import ApplyTransformToKey
  from .util import ImagenetAugmentTransform
  from .util import ImagenetNoAugmentTransform
  from .util import GenericImageTransform
  from .util import TupleToMapTransform
label_desc: Use image node_id 1 for calling a global function with example usage: build_transform(transform_config) and returns: transform