parameters: (self,
                 label_dimension,
                 loss_fn,
                 link_fn,
                 logits_dimension=None,
                 label_name=None,
                 weight_column_name=None,
                 enable_centered_bias=False,
                 head_name=None)
docstring: `Head` for regression.
  
      Args:
        label_dimension: Number of regression labels per example. This is the
          size of the last dimension of the labels `Tensor` (typically, this has
          shape `[batch_size, label_dimension]`).
        loss_fn: Loss function, takes logits and labels and returns loss.
        link_fn: Link function, takes a logits tensor and returns the output.
        logits_dimension: Number of logits per example. This is the
          size of the last dimension of the logits `Tensor` (typically, this has
          shape `[batch_size, label_dimension]`).
          Default value: `label_dimension`.
        label_name: String, name of the key in label dict. Can be null if label
            is a tensor (single headed models).
        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.
        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.
        head_name: name of the head. Predictions, summary and metrics keys are
          suffixed by `"/" + head_name` and the default variable scope is
          `head_name`.
docstring_summary: `Head` for regression.
docstring_tokens: Head for regression .
function: def __init__(self,
                 label_dimension,
                 loss_fn,
                 link_fn,
                 logits_dimension=None,
                 label_name=None,
                 weight_column_name=None,
                 enable_centered_bias=False,
                 head_name=None):
      """`Head` for regression.
  
      Args:
        label_dimension: Number of regression labels per example. This is the
          size of the last dimension of the labels `Tensor` (typically, this has
          shape `[batch_size, label_dimension]`).
        loss_fn: Loss function, takes logits and labels and returns loss.
        link_fn: Link function, takes a logits tensor and returns the output.
        logits_dimension: Number of logits per example. This is the
          size of the last dimension of the logits `Tensor` (typically, this has
          shape `[batch_size, label_dimension]`).
          Default value: `label_dimension`.
        label_name: String, name of the key in label dict. Can be null if label
            is a tensor (single headed models).
        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.
        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.
        head_name: name of the head. Predictions, summary and metrics keys are
          suffixed by `"/" + head_name` and the default variable scope is
          `head_name`.
      """
      super(_RegressionHead, self).__init__(
          problem_type=constants.ProblemType.LINEAR_REGRESSION,
          logits_dimension=(logits_dimension if logits_dimension is not None
                            else label_dimension),
          label_name=label_name,
          weight_column_name=weight_column_name,
          head_name=head_name)
  
      self._loss_fn = loss_fn
      self._link_fn = link_fn
      self._enable_centered_bias = enable_centered_bias
function_tokens: def __init__ ( self , label_dimension , loss_fn , link_fn , logits_dimension = None , label_name = None , weight_column_name = None , enable_centered_bias = False , head_name = None ) : super ( _RegressionHead , self ) . __init__ ( problem_type = constants . ProblemType . LINEAR_REGRESSION , logits_dimension = ( logits_dimension if logits_dimension is not None else label_dimension ) , label_name = label_name , weight_column_name = weight_column_name , head_name = head_name ) self . _loss_fn = loss_fn self . _link_fn = link_fn self . _enable_centered_bias = enable_centered_bias