docstr: Compute reliability of data set under perturbed hypothesis.
docstr_tok: ["Compute","reliability","of","data","set","under","perturbed","hypothesis","."]
code: def _compute_perturbed_reliability(
      self, data, labels, weights, bias, perturbation
  ):
      """Compute reliability of data set under perturbed hypothesis."""
      weights_perturbed = weights + perturbation * tf.random.normal(
          weights.shape, seed=test_util.test_seed()
      )
      logits_perturbed = tf.matmul(data, weights_perturbed)
      logits_perturbed += tf.expand_dims(bias, 0)
  
      _, _, reliability = calibration.brier_decomposition(
          labels=labels, logits=logits_perturbed
      )
  
      return reliability
  
imports: import itertools
  from absl import logging
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.stats import calibration
label_desc: Use image node_id 3 for calling the CalibrationTest obj's underlying member method code with example usage: obj._compute_perturbed_reliability(data, labels, weights, bias, perturbation) and returns: reliability