class_docstr: Encodes each NoteSequence example and emits the results in a tf.Example.
class_docstr_tok: ["Encodes","each","NoteSequence","example","and","emits","the","results","in","a","tf.Example","."]
code: def __init__(
      self, config_name, filters, *unused_args, **unused_kwargs
  ):
      super(ExtractExamplesDoFn, self).__init__(
          *unused_args, **unused_kwargs
      )
      self._config = configs.CONFIG_MAP[config_name]
      self._config.data_converter.max_outputs_per_notesequence = None
      self._filters = filters
  
file_path: magenta/magenta/models/music_vae/preprocess_tfrecord.py
imports: import typing
  from absl import app
  from absl import flags
  from absl import logging
  import apache_beam
  from apache_beam import typehints
  from apache_beam.metrics import Metrics
  from magenta.models.music_vae import configs
  import note_seq
  from note_seq.sequences_lib import split_note_sequence_on_time_changes
  import numpy
label_desc: Use image node_id 1 to create a new ExtractExamplesDoFn object from inherited base classes: beam with example: obj = ExtractExamplesDoFn(config_name, filters)