class_docstr: Abstract base class for observations = N(nn_out, noise_scale).
class_docstr_tok: ["Abstract","base","class","for","observations","=","N","(","nn_out",",","noise_scale",")","."]
code: def dist(self, params, nn_out):
      return normal_lib.Normal(loc=nn_out, scale=params["noise_scale"])
  
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 NormalLikelihoodFixedNoise obj's underlying member method code with example usage: obj.dist(params, nn_out) and returns: normal_lib