class_docstr: Stores a configuration for an event sequence RNN.
  
  Only one of `steps_per_quarter` or `steps_per_second` will be applicable for
  any particular model.
  
  Attributes:
    details: The GeneratorDetails message describing the config.
    encoder_decoder: The EventSequenceEncoderDecoder or
        ConditionalEventSequenceEncoderDecoder object to use.
    hparams: The HParams containing hyperparameters to use. Will be merged with
        default hyperparameter values.
    steps_per_quarter: The integer number of quantized time steps per quarter
        note to use.
    steps_per_second: The integer number of quantized time steps per second to
        use.
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code: def __init__(
      self,
      details,
      encoder_decoder,
      hparams,
      steps_per_quarter=4,
      steps_per_second=100,
  ):
      hparams_dict = {
          "batch_size": 64,
          "rnn_layer_sizes": [128, 128],
          "dropout_keep_prob": 1.0,
          "attn_length": 0,
          "clip_norm": 3,
          "learning_rate": 0.001,
          "residual_connections": False,
          "use_cudnn": False,
      }
      hparams_dict.update(hparams.values())
  
      self.details = details
      self.encoder_decoder = encoder_decoder
      self.hparams = contrib_training.HParams(**hparams_dict)
      self.steps_per_quarter = steps_per_quarter
      self.steps_per_second = steps_per_second
  
imports: import collections
  import copy
  import functools
  from magenta.common import beam_search
  from magenta.common import state_util
  from magenta.contrib import training
  from magenta.models.shared import events_rnn_graph
  from magenta.models.shared import model
  import note_seq
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new EventSequenceRnnConfig object from inherited base classes: object with example: obj = EventSequenceRnnConfig(details, encoder_decoder, hparams, steps_per_quarter, steps_per_second)