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: Runs the pipeline on the given input.
  
  Args:
    input_object: An object or dictionary mapping names to objects.
        The object types must match `input_type`.
  
  Returns:
    If `output_type` is a class, `transform` returns a list of objects
    which are all that type. If `output_type` is a dictionary mapping
    names to classes, `transform` returns a dictionary mapping those
    same names to lists of objects that are the type mapped to each name.
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code: def transform(self, input_object):
      """Runs the pipeline on the given input.
  
      Args:
        input_object: An object or dictionary mapping names to objects.
            The object types must match `input_type`.
  
      Returns:
        If `output_type` is a class, `transform` returns a list of objects
        which are all that type. If `output_type` is a dictionary mapping
        names to classes, `transform` returns a dictionary mapping those
        same names to lists of objects that are the type mapped to each name.
      """
      pass
  
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 7 for calling the Pipeline obj's underlying member method code with example usage: obj.transform(input_object) without return types