class_docstr: An abstract class for data processing pipelines that transform datasets.
  
  A Pipeline can transform one or many inputs to one or many outputs. When there
  are many inputs or outputs, each input/output is assigned a string name.
  
  The `transform` method converts a given input or dictionary of inputs to
  a list of transformed outputs, or a dictionary mapping names to lists of
  transformed outputs for each name.
  
  The `get_stats` method returns any Statistics that were collected during the
  last call to `transform`. These Statistics can give feedback about why any
  data was discarded and what the input data is like.
  
  `Pipeline` implementers should call `_set_stats` from within `transform` to
  set the Statistics that will be returned by the next call to `get_stats`.
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docstr: The string name of this pipeline.
docstr_tok: ["The","string","name","of","this","pipeline","."]
code: def name(self):
      """The string name of this pipeline."""
      return self._name
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import inspect
  import os.path
  from magenta.pipelines import statistics
  import six
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling the Pipeline obj's underlying member method code with example usage: obj.name() and returns: self