parameters: (self,
                 feature_columns,
                 model_dir=None,
                 label_dimension=1,
                 weight_column=None,
                 optimizer='Ftrl',
                 config=None,
                 partitioner=None)
docstring: Initializes a `LinearRegressor` instance.
  
      Args:
        feature_columns: An iterable containing all the feature columns used by
          the model. All items in the set should be instances of classes derived
          from `FeatureColumn`.
        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.
        label_dimension: Number of regression targets per example. This is the
          size of the last dimension of the labels and logits `Tensor` objects
          (typically, these have shape `[batch_size, label_dimension]`).
        weight_column: A string or a `_NumericColumn` created by
          `tf.feature_column.numeric_column` defining feature column representing
          weights. It is used to down weight or boost examples during training. It
          will be multiplied by the loss of the example. If it is a string, it is
          used as a key to fetch weight tensor from the `features`. If it is a
          `_NumericColumn`, raw tensor is fetched by key `weight_column.key`,
          then weight_column.normalizer_fn is applied on it to get weight tensor.
        optimizer: An instance of `tf.Optimizer` used to train the model. Defaults
          to FTRL optimizer.
        config: `RunConfig` object to configure the runtime settings.
        partitioner: Optional. Partitioner for input layer.
docstring_summary: Initializes a `LinearRegressor` instance.
docstring_tokens: Initializes a LinearRegressor instance .
function: def __init__(self,
                 feature_columns,
                 model_dir=None,
                 label_dimension=1,
                 weight_column=None,
                 optimizer='Ftrl',
                 config=None,
                 partitioner=None):
      """Initializes a `LinearRegressor` instance.
  
      Args:
        feature_columns: An iterable containing all the feature columns used by
          the model. All items in the set should be instances of classes derived
          from `FeatureColumn`.
        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.
        label_dimension: Number of regression targets per example. This is the
          size of the last dimension of the labels and logits `Tensor` objects
          (typically, these have shape `[batch_size, label_dimension]`).
        weight_column: A string or a `_NumericColumn` created by
          `tf.feature_column.numeric_column` defining feature column representing
          weights. It is used to down weight or boost examples during training. It
          will be multiplied by the loss of the example. If it is a string, it is
          used as a key to fetch weight tensor from the `features`. If it is a
          `_NumericColumn`, raw tensor is fetched by key `weight_column.key`,
          then weight_column.normalizer_fn is applied on it to get weight tensor.
        optimizer: An instance of `tf.Optimizer` used to train the model. Defaults
          to FTRL optimizer.
        config: `RunConfig` object to configure the runtime settings.
        partitioner: Optional. Partitioner for input layer.
      """
      head = head_lib._regression_head_with_mean_squared_error_loss(  # pylint: disable=protected-access
          label_dimension=label_dimension, weight_column=weight_column)
      def _model_fn(features, labels, mode, config):
        return _linear_model_fn(
            features=features,
            labels=labels,
            mode=mode,
            head=head,
            feature_columns=tuple(feature_columns or []),
            optimizer=optimizer,
            partitioner=partitioner,
            config=config)
      super(LinearRegressor, self).__init__(
          model_fn=_model_fn,
          model_dir=model_dir,
          config=config)
function_tokens: def __init__ ( self , feature_columns , model_dir = None , label_dimension = 1 , weight_column = None , optimizer = 'Ftrl' , config = None , partitioner = None ) : head = head_lib . _regression_head_with_mean_squared_error_loss ( # pylint: disable=protected-access label_dimension = label_dimension , weight_column = weight_column ) def _model_fn ( features , labels , mode , config ) : return _linear_model_fn ( features = features , labels = labels , mode = mode , head = head , feature_columns = tuple ( feature_columns or [ ] ) , optimizer = optimizer , partitioner = partitioner , config = config ) super ( LinearRegressor , self ) . __init__ ( model_fn = _model_fn , model_dir = model_dir , config = config )