class_docstr: Multivariate diagonal normal distribution RNN cell.
  
  The model is an LSTM-based recurrent function that computes the
  parameters for a multivariate normal distribution at each timestep
  `t`.
class_docstr_tok: ["Multivariate","diagonal","normal","distribution","RNN","cell",".","The","model","is","an","LSTM-based","recurrent","function","that","computes","the","parameters","for","a","multivariate","normal","distribution","at","each","timestep","`","t","`","."]
docstr: Returns an initial state for the LSTM cell.
  
  Args:
    sample_batch_shape: A 0D or 1D tensor of the combined sample and
      batch shape.
  
  Returns:
    A tuple of the initial previous output at timestep 0 of shape
    [sample_batch_shape, dimensions], and the cell state.
code: def zero_state(self, sample_batch_shape=()):
      """Returns an initial state for the LSTM cell.
  
      Args:
        sample_batch_shape: A 0D or 1D tensor of the combined sample and
          batch shape.
  
      Returns:
        A tuple of the initial previous output at timestep 0 of shape
        [sample_batch_shape, dimensions], and the cell state.
      """
      h0 = tf1.zeros([1, self.hidden_size])
      c0 = tf1.zeros([1, self.hidden_size])
      combined_shape = tf1.concat(
          (
              tf1.convert_to_tensor(
                  value=sample_batch_shape, dtype=tf1.int32
              ),
              [self.dimensions],
          ),
          axis=-1,
      )
      previous_output = tf1.zeros(combined_shape)
      return previous_output, (h0, c0)
  
imports: from datetime import datetime
  import functools
  from absl import app
  from absl import flags
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.examples import sprites_dataset
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 2 for calling the LearnableMultivariateNormalDiagCell obj's underlying member method code with example usage: obj.zero_state(sample_batch_shape) and returns: previous_output,  h0,  c0