(self, z, length=None, temperature=1.0, c_input=None)
Decodes a collection of latent vectors into NoteSequences.

    Args:
      z: A collection of latent vectors to decode.
      length: The maximum length of a sample in decoder iterations. Required
        if end tokens are not being used.
      temperature: The softmax temperature to use (if applicable).
      c_input: Control sequence (if applicable).
    Returns:
      A list of decodings as NoteSequence objects.
    Raises:
      RuntimeError: If called for a non-conditional model.
      ValueError: If `length` is not specified and an end token is not being
        used.
Decodes a collection of latent vectors into NoteSequences.
Decodes a collection of latent vectors into NoteSequences .
def decode(self, z, length=None, temperature=1.0, c_input=None):
    """Decodes a collection of latent vectors into NoteSequences.

    Args:
      z: A collection of latent vectors to decode.
      length: The maximum length of a sample in decoder iterations. Required
        if end tokens are not being used.
      temperature: The softmax temperature to use (if applicable).
      c_input: Control sequence (if applicable).
    Returns:
      A list of decodings as NoteSequence objects.
    Raises:
      RuntimeError: If called for a non-conditional model.
      ValueError: If `length` is not specified and an end token is not being
        used.
    """
    tensors = self.decode_to_tensors(z, length, temperature, c_input)
    if self._c_input is not None:
      return self._config.data_converter.from_tensors(
          tensors,
          np.tile(
              np.expand_dims(c_input, 0),
              [self._config.hparams.batch_size, 1, 1]))
    else:
      return self._config.data_converter.from_tensors(tensors)
def decode ( self , z , length = None , temperature = 1.0 , c_input = None ) : tensors = self . decode_to_tensors ( z , length , temperature , c_input ) if self . _c_input is not None : return self . _config . data_converter . from_tensors ( tensors , np . tile ( np . expand_dims ( c_input , 0 ) , [ self . _config . hparams . batch_size , 1 , 1 ] ) ) else : return self . _config . data_converter . from_tensors ( tensors )