tensorflow_lattice/python/estimators.py [601:622]:
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                                 feature_columns, mode, head, model_config,
                                 optimizer, config, dtype):
  """Calibrated Lattice Model."""
  del config
  if label_dimension != 1:
    raise ValueError('Only 1-dimensional output is supported.')

  # Get input tensors and corresponding feature configs.
  transformed_features = transform_features(features, feature_columns)
  feature_names = list(transformed_features.keys())
  input_tensors = [
      transformed_features[feature_name] for feature_name in feature_names
  ]
  # Reconstruct feature_config in order of feature_names
  feature_configs = [
      model_config.feature_config_by_name(feature_name)
      for feature_name in feature_names
  ]
  del model_config.feature_configs[:]
  model_config.feature_configs.extend(feature_configs)

  training = (mode == tf.estimator.ModeKeys.TRAIN)
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tensorflow_lattice/python/estimators.py [645:666]:
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                                feature_columns, mode, head, model_config,
                                optimizer, config, dtype):
  """Calibrated Linear Model."""
  del config
  if label_dimension != 1:
    raise ValueError('Only 1-dimensional output is supported.')

  # Get input tensors and corresponding feature configs.
  transformed_features = transform_features(features, feature_columns)
  feature_names = list(transformed_features.keys())
  input_tensors = [
      transformed_features[feature_name] for feature_name in feature_names
  ]
  # Reconstruct feature_config in order of feature_names
  feature_configs = [
      model_config.feature_config_by_name(feature_name)
      for feature_name in feature_names
  ]
  del model_config.feature_configs[:]
  model_config.feature_configs.extend(feature_configs)

  training = (mode == tf.estimator.ModeKeys.TRAIN)
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