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: Constructs a learnable multivariate diagonal normal cell.
  
  Args:
    dimensions: An integer corresponding to the dimensionality of the
      distribution.
    hidden_size: Dimensionality of the LSTM function parameters.
code: def __init__(self, dimensions, hidden_size):
      """Constructs a learnable multivariate diagonal normal cell.
  
      Args:
        dimensions: An integer corresponding to the dimensionality of the
          distribution.
        hidden_size: Dimensionality of the LSTM function parameters.
      """
      super(LearnableMultivariateNormalDiagCell, self).__init__()
      self.dimensions = dimensions
      self.hidden_size = hidden_size
      self.lstm_cell = tf_keras.v1.layers.LSTMCell(hidden_size)
      self.output_layer = tf_keras.v1.layers.Dense(2 * dimensions)
  
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 1 to create a new LearnableMultivariateNormalDiagCell object from inherited base classes: tf_keras.v1 with example: obj = LearnableMultivariateNormalDiagCell(dimensions, hidden_size)