class_docstr: A class that knows how to compute the likelihood of some data.
class_docstr_tok: ["A","class","that","knows","how","to","compute","the","likelihood","of","some","data","."]
docstr: Like BayesianModule::distributions but for the model's parameters.
docstr_tok: ["Like","BayesianModule",":",":distributions","but","for","the","model","'s","parameters","."]
code: def distributions(self):
      """Like BayesianModule::distributions but for the model's parameters."""
      return {}
  
imports: import dataclasses
  from typing import Any
  import jax
  from tensorflow_probability.substrates.jax.bijectors import softplus
  from tensorflow_probability.substrates.jax.distributions import distribution
  from tensorflow_probability.substrates.jax.distributions import inflated
  from tensorflow_probability.substrates.jax.distributions import logistic
  from tensorflow_probability.substrates.jax.distributions import lognormal
  from tensorflow_probability.substrates.jax.distributions import negative_binomial
  from tensorflow_probability.substrates.jax.distributions import normal
  from tensorflow_probability.substrates.jax.distributions import transformed_distribution
label_desc: Use image node_id 4 for calling the LikelihoodModel obj's underlying member method code with example usage: obj.distributions() and returns: dict