class_docstr: Apply some functions to the mini-batch
  
  Examples:
      >>> kwargs = {"process": [{"type": "fbank",
      ...                        "n_mels": 80,
      ...                        "fs": 16000},
      ...                       {"type": "cmvn",
      ...                        "stats": "data/train/cmvn.ark",
      ...                        "norm_vars": True},
      ...                       {"type": "delta", "window": 2, "order": 2}]}
      >>> transform = Transformation(kwargs)
      >>> bs = 10
      >>> xs = [np.random.randn(100, 80).astype(np.float32)
      ...       for _ in range(bs)]
      >>> xs = transform(xs)
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code: def __repr__(self):
      rep = "\n" + "\n".join(
          "    {}: {}".format(k, v) for k, v in self.functions.items()
      )
      return "{}({})".format(self.__class__.__name__, rep)
  
file_path: espnet/espnet/transform/transformation.py
imports: import copy
  import io
  import logging
  from collections import OrderedDict
  from collections.abc import Sequence
  from inspect import signature
  import yaml
  from espnet.utils.dynamic_import import dynamic_import
label_desc: Use image node_id 2 for calling the Transformation obj's underlying member method code with example usage: obj.__repr__() and returns: str