class_docstr: Base class for Deterministic distributions.
class_docstr_tok: ["Base","class","for","Deterministic","distributions","."]
code: def _entropy(self):
      entropy_dtype = (
          self.dtype
          if dtype_util.is_floating(self.dtype)
          else tf.float32
      )
      return tf.zeros(self.batch_shape_tensor(), dtype=entropy_dtype)
  
imports: import abc
  import six
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 6 for calling the _BaseDeterministic obj's underlying member method code with example usage: obj._entropy() and returns: tf