parameters: (self,
                 learner_config,
                 examples_per_layer,
                 head,
                 num_trees=None,
                 feature_columns=None,
                 weight_column_name=None,
                 model_dir=None,
                 config=None,
                 label_keys=None,
                 feature_engineering_fn=None,
                 logits_modifier_function=None,
                 center_bias=True,
                 output_leaf_index=False,
                 num_quantiles=100)
docstring: Initializes a core version of GradientBoostedDecisionTreeEstimator.
  
      Args:
        learner_config: A config for the learner.
        examples_per_layer: Number of examples to accumulate before growing a
          layer. It can also be a function that computes the number of examples
          based on the depth of the layer that's being built.
        head: `Head` instance.
        num_trees: An int, number of trees to build.
        feature_columns: A list of feature columns.
        weight_column_name: Name of the column for weights, or None if not
          weighted.
        model_dir: Directory for model exports, etc.
        config: `RunConfig` object to configure the runtime settings.
        label_keys: Optional list of strings with size `[n_classes]` defining the
          label vocabulary. Only supported for `n_classes` > 2.
        feature_engineering_fn: Feature engineering function. Takes features and
          labels which are the output of `input_fn` and returns features and
          labels which will be fed into the model.
        logits_modifier_function: A modifier function for the logits.
        center_bias: Whether a separate tree should be created for first fitting
          the bias.
        output_leaf_index: whether to output leaf indices along with predictions
          during inference. The leaf node indexes are available in predictions
          dict by the key 'leaf_index'. For example, result_dict =
          classifier.predict(...)
          for example_prediction_result in result_dict: # access leaf index list
            by example_prediction_result["leaf_index"] # which contains one leaf
            index per tree
        num_quantiles: Number of quantiles to build for numeric feature values.
docstring_summary: Initializes a core version of GradientBoostedDecisionTreeEstimator.
docstring_tokens: Initializes a core version of GradientBoostedDecisionTreeEstimator .
function: def __init__(self,
                 learner_config,
                 examples_per_layer,
                 head,
                 num_trees=None,
                 feature_columns=None,
                 weight_column_name=None,
                 model_dir=None,
                 config=None,
                 label_keys=None,
                 feature_engineering_fn=None,
                 logits_modifier_function=None,
                 center_bias=True,
                 output_leaf_index=False,
                 num_quantiles=100):
      """Initializes a core version of GradientBoostedDecisionTreeEstimator.
  
      Args:
        learner_config: A config for the learner.
        examples_per_layer: Number of examples to accumulate before growing a
          layer. It can also be a function that computes the number of examples
          based on the depth of the layer that's being built.
        head: `Head` instance.
        num_trees: An int, number of trees to build.
        feature_columns: A list of feature columns.
        weight_column_name: Name of the column for weights, or None if not
          weighted.
        model_dir: Directory for model exports, etc.
        config: `RunConfig` object to configure the runtime settings.
        label_keys: Optional list of strings with size `[n_classes]` defining the
          label vocabulary. Only supported for `n_classes` > 2.
        feature_engineering_fn: Feature engineering function. Takes features and
          labels which are the output of `input_fn` and returns features and
          labels which will be fed into the model.
        logits_modifier_function: A modifier function for the logits.
        center_bias: Whether a separate tree should be created for first fitting
          the bias.
        output_leaf_index: whether to output leaf indices along with predictions
          during inference. The leaf node indexes are available in predictions
          dict by the key 'leaf_index'. For example, result_dict =
          classifier.predict(...)
          for example_prediction_result in result_dict: # access leaf index list
            by example_prediction_result["leaf_index"] # which contains one leaf
            index per tree
        num_quantiles: Number of quantiles to build for numeric feature values.
      """
  
      def _model_fn(features, labels, mode, config):
        return model.model_builder(
            features=features,
            labels=labels,
            mode=mode,
            config=config,
            params={
                'head': head,
                'feature_columns': feature_columns,
                'learner_config': learner_config,
                'num_trees': num_trees,
                'weight_column_name': weight_column_name,
                'examples_per_layer': examples_per_layer,
                'center_bias': center_bias,
                'logits_modifier_function': logits_modifier_function,
                'use_core_libs': True,
                'output_leaf_index': output_leaf_index,
                'override_global_step_value': None,
                'num_quantiles': num_quantiles,
            },
            output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC)
  
      super(CoreGradientBoostedDecisionTreeEstimator, self).__init__(
          model_fn=_model_fn, model_dir=model_dir, config=config)
function_tokens: def __init__ ( self , learner_config , examples_per_layer , head , num_trees = None , feature_columns = None , weight_column_name = None , model_dir = None , config = None , label_keys = None , feature_engineering_fn = None , logits_modifier_function = None , center_bias = True , output_leaf_index = False , num_quantiles = 100 ) : def _model_fn ( features , labels , mode , config ) : return model . model_builder ( features = features , labels = labels , mode = mode , config = config , params = { 'head' : head , 'feature_columns' : feature_columns , 'learner_config' : learner_config , 'num_trees' : num_trees , 'weight_column_name' : weight_column_name , 'examples_per_layer' : examples_per_layer , 'center_bias' : center_bias , 'logits_modifier_function' : logits_modifier_function , 'use_core_libs' : True , 'output_leaf_index' : output_leaf_index , 'override_global_step_value' : None , 'num_quantiles' : num_quantiles , } , output_type = model . ModelBuilderOutputType . ESTIMATOR_SPEC ) super ( CoreGradientBoostedDecisionTreeEstimator , self ) . __init__ ( model_fn = _model_fn , model_dir = model_dir , config = config )