def _evaluate_batch_log_likelihood(self, event_sequences, inputs, initial_state):
    'Evaluates the log likelihood of a batch of event sequences.\n\n    Args:\n      event_sequences: A list of event sequences, each of which is a Python\n          list-like object. The list of event sequences should have length equal\n          to `self._batch_size()`.\n      inputs: A Python list of model inputs, with length equal to\n          `self._batch_size()`.\n      initial_state: A numpy array containing the initial RNN state, where\n          `initial_state.shape[0]` is equal to `self._batch_size()`.\n\n    Returns:\n      A Python list containing the log likelihood of each sequence in\n      `event_sequences`.\n    '
    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}
    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)
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`.
def _evaluate_batch_log_likelihood(self, event_sequences, inputs, initial_state):
    'Evaluates the log likelihood of a batch of event sequences.\n\n    Args:\n      event_sequences: A list of event sequences, each of which is a Python\n          list-like object. The list of event sequences should have length equal\n          to `self._batch_size()`.\n      inputs: A Python list of model inputs, with length equal to\n          `self._batch_size()`.\n      initial_state: A numpy array containing the initial RNN state, where\n          `initial_state.shape[0]` is equal to `self._batch_size()`.\n\n    Returns:\n      A Python list containing the log likelihood of each sequence in\n      `event_sequences`.\n    '
    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}
    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)