text: # Lint as: python2, python3
  # Copyright 2020 The TensorFlow Probability Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  r"""Ground truth values for `german_credit_numeric_logistic_regression`.
  
  Automatically generated using the command:
  
  ```
  bazel run //tools/inference_gym_ground_truth:get_ground_truth -- \
    --target \
    german_credit_numeric_logistic_regression \
  ```
  """
  
  import numpy as np
  
  IDENTITY_MEAN = np.array([
      -0.7351048553686668,
      0.41854235448568405,
      -0.4140361022849266,
      0.12687262544490638,
      -0.36453584119271787,
      -0.1786990235929577,
      -0.1528721119830067,
      0.0130935930161775,
      0.18071618213137836,
      -0.11077840748059746,
      -0.22434837978228872,
      0.12239538160879522,
      0.028775849958589513,
      -0.13628208974727007,
      -0.29222110498210363,
      0.2783575897857832,
      -0.2996277708109526,
      0.30372734184257766,
      0.27038791575592425,
      0.12251564641333557,
      -0.062930540861664,
      -0.09271734036278598,
      -0.025386265018982113,
      -0.022952091856998594,
      -1.2033366774193333,
  ]).reshape((25,))
  
  IDENTITY_MEAN_STANDARD_ERROR = np.array([
      5.842293909946494e-05,
      7.242951181494356e-05,
      6.287678982885978e-05,
      7.585193280148798e-05,
      6.115211849593741e-05,
      6.021116416974708e-05,
      5.204191507761724e-05,
      5.860998969304511e-05,
      7.29503297927934e-05,
      6.490239025755679e-05,
      4.990373753354614e-05,
      6.283413887066306e-05,
      5.430645722326503e-05,
      6.406386782855579e-05,
      7.892840871425272e-05,
      5.308342894035861e-05,
      6.703376967839617e-05,
      8.521129854167403e-05,
      7.765561215475798e-05,
      0.00010413139262019992,
      0.00010841073917598099,
      6.237296545620734e-05,
      9.654815236395932e-05,
      9.49005719330975e-05,
      6.225181243823337e-05,
  ]).reshape((25,))
  
  IDENTITY_STANDARD_DEVIATION = np.array([
      0.0898313720177512,
      0.10433392890125515,
      0.09494358976312321,
      0.10821559696336329,
      0.09451801286114327,
      0.09209986501802636,
      0.08194570882231808,
      0.09096249386093944,
      0.10427142118193244,
      0.09706664883314095,
      0.07886872456716118,
      0.09415178440121623,
      0.08568162266412561,
      0.094635647710843,
      0.11794843143366165,
      0.08278578466826157,
      0.10338649406760281,
      0.12112997506000497,
      0.11129990766341216,
      0.13748697192324197,
      0.14311733514054628,
      0.09036915198426924,
      0.12757406812435373,
      0.12488837996746398,
      0.09189586059142167,
  ]).reshape((25,))
  
  