class_docstr: Decorator for preprocessing ops, which adds support for image batches.
  
  Note: Doesn't support decorating ops which add new fields in data.
class_docstr_tok: ["Decorator","for","preprocessing","ops",",","which","adds","support","for","image","batches",".","Note",":","Does","n't","support","decorating","ops","which","add","new","fields","in","data","."]
code: def __call__(self, get_pp_fn):
      def get_batch_pp_fn(*args, **kwargs):
          """Preprocessing function that supports batched images."""
  
          def _batch_pp_fn(image, *a, **kw):
              orig_image_pp_fn = get_pp_fn(*args, **kwargs)
              orig_image_pp_fn = functools.partial(
                  orig_image_pp_fn, *a, **kw
              )
              return tf_apply_to_image_or_images(
                  orig_image_pp_fn, image, dtype=self.output_dtype
              )
  
          return _batch_pp_fn
  
      return get_batch_pp_fn
  
file_path: scenic/scenic/dataset_lib/big_transfer/preprocessing/utils.py
imports: from collections import abc
  import functools
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the BatchedImagePreprocessing obj's underlying member method code with example usage: obj.__call__(get_pp_fn) and returns: get_batch_pp_fn, _batch_pp_fn, tf_apply_to_image_or_images