class_docstr: Learnable multivariate diagonal normal distribution.
  
  The model is a multivariate normal distribution with learnable
  `mean` and `stddev` parameters.
class_docstr_tok: ["Learnable","multivariate","diagonal","normal","distribution",".","The","model","is","a","multivariate","normal","distribution","with","learnable","`","mean","`","and","`","stddev","`","parameters","."]
docstr: Runs the model to generate multivariate normal distribution.
  
  Args:
    inputs: Unused.
  
  Returns:
    A MultivariateNormalDiag distribution with event shape
    [dimensions], batch shape [], and sample shape [sample_shape,
    dimensions].
code: def call(self, inputs):
      """Runs the model to generate multivariate normal distribution.
  
      Args:
        inputs: Unused.
  
      Returns:
        A MultivariateNormalDiag distribution with event shape
        [dimensions], batch shape [], and sample shape [sample_shape,
        dimensions].
      """
      del inputs  # unused
      with tf1.name_scope(self._name):
          return tfd.MultivariateNormalDiag(self.loc, self.scale_diag)
  
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 3 for calling the LearnableMultivariateNormalDiag obj's underlying member method code with example usage: obj.call(inputs) and returns: tfd