parameters: (self,
                 dnn_hidden_units,
                 dnn_feature_columns,
                 tree_learner_config,
                 num_trees,
                 tree_examples_per_layer,
                 head,
                 model_dir=None,
                 config=None,
                 feature_engineering_fn=None,
                 dnn_optimizer="Adagrad",
                 dnn_activation_fn=nn.relu,
                 dnn_dropout=None,
                 dnn_input_layer_partitioner=None,
                 dnn_input_layer_to_tree=True,
                 dnn_steps_to_train=10000,
                 predict_with_tree_only=False,
                 tree_feature_columns=None,
                 tree_center_bias=False,
                 dnn_to_tree_distillation_param=None,
                 use_core_versions=False,
                 override_global_step_value=None)
docstring: Initializes a DNNBoostedTreeCombinedEstimator instance.
  
      Args:
        dnn_hidden_units: List of hidden units per layer for DNN.
        dnn_feature_columns: An iterable containing all the feature columns
          used by the model's DNN.
        tree_learner_config: A config for the tree learner.
        num_trees: Number of trees to grow model to after training DNN.
        tree_examples_per_layer: Number of examples to accumulate before
          growing the tree a layer. This value has a big impact on model
          quality and should be set equal to the number of examples in
          training dataset if possible. 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.
        model_dir: Directory for model exports.
        config: `RunConfig` of the estimator.
        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.
        dnn_optimizer: string, `Optimizer` object, or callable that defines the
          optimizer to use for training the DNN. If `None`, will use the Adagrad
          optimizer with default learning rate.
        dnn_activation_fn: Activation function applied to each layer of the DNN.
          If `None`, will use `tf.nn.relu`.
        dnn_dropout: When not `None`, the probability to drop out a given
          unit in the DNN.
        dnn_input_layer_partitioner: Partitioner for input layer of the DNN.
          Defaults to `min_max_variable_partitioner` with `min_slice_size`
          64 << 20.
        dnn_input_layer_to_tree: Whether to provide the DNN's input layer
        as a feature to the tree.
        dnn_steps_to_train: Number of steps to train dnn for before switching
          to gbdt.
        predict_with_tree_only: Whether to use only the tree model output as the
          final prediction.
        tree_feature_columns: An iterable containing all the feature columns
          used by the model's boosted trees. If dnn_input_layer_to_tree is
          set to True, these features are in addition to dnn_feature_columns.
        tree_center_bias: Whether a separate tree should be created for
          first fitting the bias.
        dnn_to_tree_distillation_param: A Tuple of (float, loss_fn), where the
          float defines the weight of the distillation loss, and the loss_fn, for
          computing distillation loss, takes dnn_logits, tree_logits and weight
          tensor. If the entire tuple is None, no distillation will be applied. If
          only the loss_fn is None, we will take the sigmoid/softmax cross entropy
          loss be default. When distillation is applied, `predict_with_tree_only`
          will be set to True.
        use_core_versions: Whether feature columns and loss are from the core (as
          opposed to contrib) version of tensorflow.
        override_global_step_value: If after the training is done, global step
          value must be reset to this value. This is particularly useful for hyper
          parameter tuning, which can't recognize early stopping due to the number
          of trees. If None, no override of global step will happen.
docstring_summary: Initializes a DNNBoostedTreeCombinedEstimator instance.
docstring_tokens: Initializes a DNNBoostedTreeCombinedEstimator instance .
function: def __init__(self,
                 dnn_hidden_units,
                 dnn_feature_columns,
                 tree_learner_config,
                 num_trees,
                 tree_examples_per_layer,
                 head,
                 model_dir=None,
                 config=None,
                 feature_engineering_fn=None,
                 dnn_optimizer="Adagrad",
                 dnn_activation_fn=nn.relu,
                 dnn_dropout=None,
                 dnn_input_layer_partitioner=None,
                 dnn_input_layer_to_tree=True,
                 dnn_steps_to_train=10000,
                 predict_with_tree_only=False,
                 tree_feature_columns=None,
                 tree_center_bias=False,
                 dnn_to_tree_distillation_param=None,
                 use_core_versions=False,
                 override_global_step_value=None):
      """Initializes a DNNBoostedTreeCombinedEstimator instance.
  
      Args:
        dnn_hidden_units: List of hidden units per layer for DNN.
        dnn_feature_columns: An iterable containing all the feature columns
          used by the model's DNN.
        tree_learner_config: A config for the tree learner.
        num_trees: Number of trees to grow model to after training DNN.
        tree_examples_per_layer: Number of examples to accumulate before
          growing the tree a layer. This value has a big impact on model
          quality and should be set equal to the number of examples in
          training dataset if possible. 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.
        model_dir: Directory for model exports.
        config: `RunConfig` of the estimator.
        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.
        dnn_optimizer: string, `Optimizer` object, or callable that defines the
          optimizer to use for training the DNN. If `None`, will use the Adagrad
          optimizer with default learning rate.
        dnn_activation_fn: Activation function applied to each layer of the DNN.
          If `None`, will use `tf.nn.relu`.
        dnn_dropout: When not `None`, the probability to drop out a given
          unit in the DNN.
        dnn_input_layer_partitioner: Partitioner for input layer of the DNN.
          Defaults to `min_max_variable_partitioner` with `min_slice_size`
          64 << 20.
        dnn_input_layer_to_tree: Whether to provide the DNN's input layer
        as a feature to the tree.
        dnn_steps_to_train: Number of steps to train dnn for before switching
          to gbdt.
        predict_with_tree_only: Whether to use only the tree model output as the
          final prediction.
        tree_feature_columns: An iterable containing all the feature columns
          used by the model's boosted trees. If dnn_input_layer_to_tree is
          set to True, these features are in addition to dnn_feature_columns.
        tree_center_bias: Whether a separate tree should be created for
          first fitting the bias.
        dnn_to_tree_distillation_param: A Tuple of (float, loss_fn), where the
          float defines the weight of the distillation loss, and the loss_fn, for
          computing distillation loss, takes dnn_logits, tree_logits and weight
          tensor. If the entire tuple is None, no distillation will be applied. If
          only the loss_fn is None, we will take the sigmoid/softmax cross entropy
          loss be default. When distillation is applied, `predict_with_tree_only`
          will be set to True.
        use_core_versions: Whether feature columns and loss are from the core (as
          opposed to contrib) version of tensorflow.
        override_global_step_value: If after the training is done, global step
          value must be reset to this value. This is particularly useful for hyper
          parameter tuning, which can't recognize early stopping due to the number
          of trees. If None, no override of global step will happen.
      """
  
      def _model_fn(features, labels, mode, config):
        return _dnn_tree_combined_model_fn(
            features=features,
            labels=labels,
            mode=mode,
            head=head,
            dnn_hidden_units=dnn_hidden_units,
            dnn_feature_columns=dnn_feature_columns,
            tree_learner_config=tree_learner_config,
            num_trees=num_trees,
            tree_examples_per_layer=tree_examples_per_layer,
            config=config,
            dnn_optimizer=dnn_optimizer,
            dnn_activation_fn=dnn_activation_fn,
            dnn_dropout=dnn_dropout,
            dnn_input_layer_partitioner=dnn_input_layer_partitioner,
            dnn_input_layer_to_tree=dnn_input_layer_to_tree,
            dnn_steps_to_train=dnn_steps_to_train,
            predict_with_tree_only=predict_with_tree_only,
            tree_feature_columns=tree_feature_columns,
            tree_center_bias=tree_center_bias,
            dnn_to_tree_distillation_param=dnn_to_tree_distillation_param,
            use_core_versions=use_core_versions,
            override_global_step_value=override_global_step_value)
  
      super(DNNBoostedTreeCombinedEstimator, self).__init__(
          model_fn=_model_fn,
          model_dir=model_dir,
          config=config,
          feature_engineering_fn=feature_engineering_fn)
function_tokens: def __init__ ( self , dnn_hidden_units , dnn_feature_columns , tree_learner_config , num_trees , tree_examples_per_layer , head , model_dir = None , config = None , feature_engineering_fn = None , dnn_optimizer = "Adagrad" , dnn_activation_fn = nn . relu , dnn_dropout = None , dnn_input_layer_partitioner = None , dnn_input_layer_to_tree = True , dnn_steps_to_train = 10000 , predict_with_tree_only = False , tree_feature_columns = None , tree_center_bias = False , dnn_to_tree_distillation_param = None , use_core_versions = False , override_global_step_value = None ) : def _model_fn ( features , labels , mode , config ) : return _dnn_tree_combined_model_fn ( features = features , labels = labels , mode = mode , head = head , dnn_hidden_units = dnn_hidden_units , dnn_feature_columns = dnn_feature_columns , tree_learner_config = tree_learner_config , num_trees = num_trees , tree_examples_per_layer = tree_examples_per_layer , config = config , dnn_optimizer = dnn_optimizer , dnn_activation_fn = dnn_activation_fn , dnn_dropout = dnn_dropout , dnn_input_layer_partitioner = dnn_input_layer_partitioner , dnn_input_layer_to_tree = dnn_input_layer_to_tree , dnn_steps_to_train = dnn_steps_to_train , predict_with_tree_only = predict_with_tree_only , tree_feature_columns = tree_feature_columns , tree_center_bias = tree_center_bias , dnn_to_tree_distillation_param = dnn_to_tree_distillation_param , use_core_versions = use_core_versions , override_global_step_value = override_global_step_value ) super ( DNNBoostedTreeCombinedEstimator , self ) . __init__ ( model_fn = _model_fn , model_dir = model_dir , config = config , feature_engineering_fn = feature_engineering_fn )