class_docstr: observations = NB(total_count = nn_out[0], logits = nn_out[1]).
class_docstr_tok: ["observations","=","NB","(","total_count","=","nn_out","[","0","]",",","logits","=","nn_out","[","1","]",")","."]
code: def num_outputs(self):
      return 2
  
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 1 for calling the NegativeBinomial obj's underlying member method code with example usage: obj.num_outputs() and returns: int