class_docstr: Base class for continuous entropy models.
  
  The basic functionality of this class is to pre-compute integer probability
  tables based on the provided `tfp.distributions.Distribution` object, which
  can then be used reliably across different platforms by the range coder and
  decoder.
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docstr: Returns the configuration of the entropy model.
  
  Returns:
    A JSON-serializable Python dict.
  
  Raises:
    RuntimeError: on attempting to call this method on an entropy model
      with `compression=False` or with `stateless=True`.
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code: def get_config(self):
      """Returns the configuration of the entropy model.
  
      Returns:
        A JSON-serializable Python dict.
  
      Raises:
        RuntimeError: on attempting to call this method on an entropy model
          with `compression=False` or with `stateless=True`.
      """
      if self.stateless or not self.compression:
          raise RuntimeError(
              "Serializing entropy models with `compression=False` or "
              "`stateless=True` is not supported."
          )
      return dict(
          coding_rank=self.coding_rank,
          compression=True,
          stateless=False,
          expected_grads=self.expected_grads,
          tail_mass=self.tail_mass,
          cdf_shapes=(self.cdf.shape[0], self.cdf_offset.shape[0]),
          bottleneck_dtype=self.bottleneck_dtype.name,
          laplace_tail_mass=float(self.laplace_tail_mass),
      )
  
imports: import abc
  from absl import logging
  import tensorflow
  from tensorflow_compression.python.distributions import helpers
  from tensorflow_compression.python.distributions import uniform_noise
  from tensorflow_compression.python.ops import gen_ops
label_desc: Use image node_id 18 for calling the ContinuousEntropyModelBase obj's underlying member method code with example usage: obj.get_config() and returns: dict