class_docstr: A mock univariate distribution with a given batch shape.
class_docstr_tok: ["A","mock","univariate","distribution","with","a","given","batch","shape","."]
code: def __init__(self, batch_shape, parameter):
      self.batch_shape = tf.TensorShape(batch_shape)
      self.event_shape = tf.TensorShape([])
      self._parameter = parameter
      entropy = np.arange(np.prod(batch_shape)).reshape(batch_shape)
      entropy *= parameter * parameter
      self._entropy = tf.constant(entropy, dtype=tf.float32)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  from six.moves import xrange
  import tensorflow.compat.v1
  import tensorflow_probability
  import tree
  from trfl import policy_gradient_ops
label_desc: Use image node_id 1 to create a new MockDistribution object from inherited base classes: object with example: obj = MockDistribution(batch_shape, parameter)