class_docstr: Distribution followed by tanh.
class_docstr_tok: ["Distribution","followed","by","tanh","."]
code: def entropy(self, seed=None):
      # We return an estimation using a single sample of the log_det_jacobian.
      # We can still do some backpropagation with this estimate.
      return (
          self.distribution.entropy()
          + self.bijector.forward_log_det_jacobian(
              self.distribution.sample(seed=seed), event_ndims=0
          )
      )
  
imports: from typing import Any, List, Optional, Sequence, Union, Callable
  import chex
  import haiku
  import jax
  import jax.numpy
  import numpy
  import tensorflow_probability
  import tensorflow_probability.substrates.jax
label_desc: Use image node_id 4 for calling the TanhTransformedDistribution obj's underlying member method code with example usage: obj.entropy(seed) and returns: unknown