parameters: (self,
                 model_dir=None,
                 weight_column_name=None,
                 linear_feature_columns=None,
                 linear_optimizer=None,
                 dnn_feature_columns=None,
                 dnn_optimizer=None,
                 dnn_hidden_units=None,
                 dnn_activation_fn=nn.relu,
                 dnn_dropout=None,
                 gradient_clip_norm=None,
                 enable_centered_bias=True,
                 target_dimension=1,
                 config=None)
docstring: Initializes a DNNLinearCombinedRegressor instance.
  
      Args:
        model_dir: Directory to save model parameters, graph and etc. This can
          also be used to load checkpoints from the directory into a estimator
          to continue training a previously saved model.
        weight_column_name: A string defining feature column name representing
          weights. It is used to down weight or boost examples during training. It
          will be multiplied by the loss of the example.
        linear_feature_columns: An iterable containing all the feature columns
          used by linear part of the model. All items in the set must be
          instances of classes derived from `FeatureColumn`.
        linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
          the linear part of the model. If `None`, will use a FTRL optimizer.
        dnn_feature_columns: An iterable containing all the feature columns used
          by deep part of the model. All items in the set must be instances of
          classes derived from `FeatureColumn`.
        dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
          the deep part of the model. If `None`, will use an Adagrad optimizer.
        dnn_hidden_units: List of hidden units per layer. All layers are fully
          connected.
        dnn_activation_fn: Activation function applied to each layer. If None,
          will use `tf.nn.relu`.
        dnn_dropout: When not None, the probability we will drop out
          a given coordinate.
        gradient_clip_norm: A float > 0. If provided, gradients are clipped
          to their global norm with this clipping ratio. See
          tf.clip_by_global_norm for more details.
        enable_centered_bias: A bool. If True, estimator will learn a centered
          bias variable for each class. Rest of the model structure learns the
          residual after centered bias.
        target_dimension: TODO(zakaria): dimension of the target for multilabels.
        config: RunConfig object to configure the runtime settings.
  
      Raises:
        ValueError: If both linear_feature_columns and dnn_features_columns are
          empty at the same time.
docstring_summary: Initializes a DNNLinearCombinedRegressor instance.
docstring_tokens: Initializes a DNNLinearCombinedRegressor instance .
function: def __init__(self,
                 model_dir=None,
                 weight_column_name=None,
                 linear_feature_columns=None,
                 linear_optimizer=None,
                 dnn_feature_columns=None,
                 dnn_optimizer=None,
                 dnn_hidden_units=None,
                 dnn_activation_fn=nn.relu,
                 dnn_dropout=None,
                 gradient_clip_norm=None,
                 enable_centered_bias=True,
                 target_dimension=1,
                 config=None):
      """Initializes a DNNLinearCombinedRegressor instance.
  
      Args:
        model_dir: Directory to save model parameters, graph and etc. This can
          also be used to load checkpoints from the directory into a estimator
          to continue training a previously saved model.
        weight_column_name: A string defining feature column name representing
          weights. It is used to down weight or boost examples during training. It
          will be multiplied by the loss of the example.
        linear_feature_columns: An iterable containing all the feature columns
          used by linear part of the model. All items in the set must be
          instances of classes derived from `FeatureColumn`.
        linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
          the linear part of the model. If `None`, will use a FTRL optimizer.
        dnn_feature_columns: An iterable containing all the feature columns used
          by deep part of the model. All items in the set must be instances of
          classes derived from `FeatureColumn`.
        dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
          the deep part of the model. If `None`, will use an Adagrad optimizer.
        dnn_hidden_units: List of hidden units per layer. All layers are fully
          connected.
        dnn_activation_fn: Activation function applied to each layer. If None,
          will use `tf.nn.relu`.
        dnn_dropout: When not None, the probability we will drop out
          a given coordinate.
        gradient_clip_norm: A float > 0. If provided, gradients are clipped
          to their global norm with this clipping ratio. See
          tf.clip_by_global_norm for more details.
        enable_centered_bias: A bool. If True, estimator will learn a centered
          bias variable for each class. Rest of the model structure learns the
          residual after centered bias.
        target_dimension: TODO(zakaria): dimension of the target for multilabels.
        config: RunConfig object to configure the runtime settings.
  
      Raises:
        ValueError: If both linear_feature_columns and dnn_features_columns are
          empty at the same time.
      """
      target_column = layers.regression_target(
          weight_column_name=weight_column_name,
          target_dimension=target_dimension)
      super(DNNLinearCombinedRegressor, self).__init__(
          model_dir=model_dir,
          linear_feature_columns=linear_feature_columns,
          linear_optimizer=linear_optimizer,
          dnn_feature_columns=dnn_feature_columns,
          dnn_optimizer=dnn_optimizer,
          dnn_hidden_units=dnn_hidden_units,
          dnn_activation_fn=dnn_activation_fn,
          dnn_dropout=dnn_dropout,
          gradient_clip_norm=gradient_clip_norm,
          enable_centered_bias=enable_centered_bias,
          target_column=target_column,
          config=config)
function_tokens: def __init__ ( self , model_dir = None , weight_column_name = None , linear_feature_columns = None , linear_optimizer = None , dnn_feature_columns = None , dnn_optimizer = None , dnn_hidden_units = None , dnn_activation_fn = nn . relu , dnn_dropout = None , gradient_clip_norm = None , enable_centered_bias = True , target_dimension = 1 , config = None ) : target_column = layers . regression_target ( weight_column_name = weight_column_name , target_dimension = target_dimension ) super ( DNNLinearCombinedRegressor , self ) . __init__ ( model_dir = model_dir , linear_feature_columns = linear_feature_columns , linear_optimizer = linear_optimizer , dnn_feature_columns = dnn_feature_columns , dnn_optimizer = dnn_optimizer , dnn_hidden_units = dnn_hidden_units , dnn_activation_fn = dnn_activation_fn , dnn_dropout = dnn_dropout , gradient_clip_norm = gradient_clip_norm , enable_centered_bias = enable_centered_bias , target_column = target_column , config = config )