class_docstr: Class for RNN event sequence generation models.
  
  Currently this class only supports generation, of both event sequences and
  note sequences (via event sequences). Support for model training will be added
  at a later time.
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docstr: Evaluates the log likelihood of a batch of event sequences.
  
  Args:
    event_sequences: A list of event sequences, each of which is a Python
        list-like object. The list of event sequences should have length equal
        to `self._batch_size()`.
    inputs: A Python list of model inputs, with length equal to
        `self._batch_size()`.
    initial_state: A numpy array containing the initial RNN state, where
        `initial_state.shape[0]` is equal to `self._batch_size()`.
  
  Returns:
    A Python list containing the log likelihood of each sequence in
    `event_sequences`.
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code: def _evaluate_batch_log_likelihood(
      self, event_sequences, inputs, initial_state
  ):
      """Evaluates the log likelihood of a batch of event sequences.
  
      Args:
        event_sequences: A list of event sequences, each of which is a Python
            list-like object. The list of event sequences should have length equal
            to `self._batch_size()`.
        inputs: A Python list of model inputs, with length equal to
            `self._batch_size()`.
        initial_state: A numpy array containing the initial RNN state, where
            `initial_state.shape[0]` is equal to `self._batch_size()`.
  
      Returns:
        A Python list containing the log likelihood of each sequence in
        `event_sequences`.
      """
      graph_inputs = self._session.graph.get_collection("inputs")[0]
      graph_initial_state = self._session.graph.get_collection(
          "initial_state"
      )
      graph_softmax = self._session.graph.get_collection("softmax")[0]
      graph_temperature = self._session.graph.get_collection(
          "temperature"
      )
  
      feed_dict = {
          graph_inputs: inputs,
          tuple(graph_initial_state): initial_state,
      }
      # For backwards compatibility, we only try to pass temperature if the
      # placeholder exists in the graph.
      if graph_temperature:
          feed_dict[graph_temperature[0]] = 1.0
      softmax = self._session.run(graph_softmax, feed_dict)
  
      return self._config.encoder_decoder.evaluate_log_likelihood(
          event_sequences, softmax
      )
  
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 7 for calling the EventSequenceRnnModel obj's underlying member method code with example usage: obj._evaluate_batch_log_likelihood(event_sequences, inputs, initial_state) and returns: self