class_docstr: Organises data transformations into a pipeline.
  
  Example
  -------
  >>> pipeline = DataPipeline(
  ...     static_data_keys=["text"],
  ...     dynamic_items=[
  ...     {"func": lambda x: x.lower(), "takes": "text", "provides": "foo"},
  ...     {"func": lambda x: x[::-1], "takes": "foo", "provides": "bar"},
  ...     ],
  ...     output_keys=["bar"],
  ... )
  >>> pipeline({"text": "Test"})
  {'bar': 'tset'}
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docstr: Use this to change the output keys.
  
  Also re-evaluates execution order.
  So if you request different outputs, some parts of the
  data pipeline may be skipped.
  
  Arguments
  ---------
  keys : dict, list, None
      List of keys (str) to produce in output.
  
      If a dict is given; it is used to map internal keys to output keys.
      From the output_keys dict key:value pairs the key appears outside,
      and value is the internal key.
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code: def set_output_keys(self, keys):
      """Use this to change the output keys.
  
      Also re-evaluates execution order.
      So if you request different outputs, some parts of the
      data pipeline may be skipped.
  
      Arguments
      ---------
      keys : dict, list, None
          List of keys (str) to produce in output.
  
          If a dict is given; it is used to map internal keys to output keys.
          From the output_keys dict key:value pairs the key appears outside,
          and value is the internal key.
      """
      self.output_mapping = self._output_keys_to_mapping(keys)
      self._exec_order = None
  
imports: import inspect
  from dataclasses import dataclass
  from speechbrain.utils.depgraph import DependencyGraph
label_desc: Use image node_id 6 for calling the DataPipeline obj's underlying member method code with example usage: obj.set_output_keys(keys) without return types