old_contents: # coding=utf-8
  # Copyright 2021 The Google Research 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.
  
  # Lint as: python3
  """Small models to be finetuned on embeddings."""
  
  from sklearn import discriminant_analysis
  from sklearn import ensemble
  from sklearn import linear_model
  
  LogisticRegression = linear_model.LogisticRegression
  LinearDiscriminantAnalysis = discriminant_analysis.LinearDiscriminantAnalysis
  RandomForestClassifier = ensemble.RandomForestClassifier
  
  
  # pylint:disable=line-too-long,unused-variable, g-no-space-after-comment
  def get_sklearn_models():
    return {
        'LogisticRegression': lambda: LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial'),
        'LogisticRegression_balanced': lambda: LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial', class_weight='balanced'),
        'LDA_LSQR_AUTO': lambda: LinearDiscriminantAnalysis(solver='lsqr', shrinkage='auto'),
        'Forest_balanced_100': lambda: RandomForestClassifier(class_weight='balanced', n_estimators=100),
        'Forest_unbalanced_100': lambda: RandomForestClassifier(class_weight=None, n_estimators=100),
    }
  # pylint:enable=line-too-long,unused-variable, g-no-space-after-comment
  
new_contents: # coding=utf-8
  # Copyright 2021 The Google Research 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.
  
  # Lint as: python3
  """Small models to be finetuned on embeddings."""
  
  from sklearn import discriminant_analysis
  from sklearn import ensemble
  from sklearn import linear_model
  
  LogisticRegression = linear_model.LogisticRegression
  LinearDiscriminantAnalysis = discriminant_analysis.LinearDiscriminantAnalysis
  RandomForestClassifier = ensemble.RandomForestClassifier
  
  
  # pylint:disable=line-too-long,unused-variable, g-no-space-after-comment
  def get_sklearn_models():
    return {
        'LogisticRegression':
            lambda: LogisticRegression(
                C=1e5, solver='lbfgs', multi_class='multinomial'),
        'LogisticRegression_balanced':
            lambda: LogisticRegression(
                C=1e5,
                solver='lbfgs',
                multi_class='multinomial',
                class_weight='balanced'),
        'LDA_LSQR_AUTO':
            lambda: LinearDiscriminantAnalysis(solver='lsqr', shrinkage='auto'),
    }
  # pylint:enable=line-too-long,unused-variable, g-no-space-after-comment
  
subject: Remove boosted trees. They're not the best on any NOSS task.