class_docstr: A Pipeline that splits NoteSequences at regular intervals.
class_docstr_tok: ["A","Pipeline","that","splits","NoteSequences","at","regular","intervals","."]
docstr: Creates a Splitter pipeline.
  
  Args:
    hop_size_seconds: Hop size in seconds that will be used to split a
        NoteSequence at regular intervals.
    name: Pipeline name.
docstr_tok: ["Creates","a","Splitter","pipeline",".","Args",":","hop_size_seconds",":","Hop","size","in","seconds","that","will","be","used","to","split","a","NoteSequence","at","regular","intervals",".","name",":","Pipeline","name","."]
code: def __init__(self, hop_size_seconds, name=None):
      """Creates a Splitter pipeline.
  
      Args:
        hop_size_seconds: Hop size in seconds that will be used to split a
            NoteSequence at regular intervals.
        name: Pipeline name.
      """
      super(Splitter, self).__init__(name=name)
      self._hop_size_seconds = hop_size_seconds
  
imports: import copy
  from magenta.pipelines import pipeline
  from magenta.pipelines import statistics
  from note_seq import constants
  from note_seq import sequences_lib
  from note_seq.protobuf import music_pb2
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new Splitter object from inherited base classes: NoteSequencePipeline with example: obj = Splitter(hop_size_seconds, name)