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: The number of outputs from the neural network the model needs.
docstr_tok: ["The","number","of","outputs","from","the","neural","network","the","model","needs","."]
code: def num_outputs(self):
      """The number of outputs from the neural network the model needs."""
      return 1
  
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 3 for calling the LikelihoodModel obj's underlying member method code with example usage: obj.num_outputs() and returns: int