parameters: (self,
                 label_name=None,
                 weight_column_name=None,
                 enable_centered_bias=False,
                 head_name=None,
                 loss_fn=None,
                 thresholds=None)
docstring: `Head` for binary classification with logistic regression.
  
      Args:
        label_name: String, name of the key in label dict. Can be `None` 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, metrics keys are
          suffixed by `"/" + head_name` and the default variable scope is
          `head_name`.
        loss_fn: Loss function.
        thresholds: thresholds for eval.
  
      Raises:
        ValueError: if n_classes is invalid.
docstring_summary: `Head` for binary classification with logistic regression.
docstring_tokens: Head for binary classification with logistic regression .
function: def __init__(self,
                 label_name=None,
                 weight_column_name=None,
                 enable_centered_bias=False,
                 head_name=None,
                 loss_fn=None,
                 thresholds=None):
      """`Head` for binary classification with logistic regression.
  
      Args:
        label_name: String, name of the key in label dict. Can be `None` 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, metrics keys are
          suffixed by `"/" + head_name` and the default variable scope is
          `head_name`.
        loss_fn: Loss function.
        thresholds: thresholds for eval.
  
      Raises:
        ValueError: if n_classes is invalid.
      """
      super(_BinaryLogisticHead, self).__init__(
          problem_type=constants.ProblemType.LOGISTIC_REGRESSION,
          logits_dimension=1,
          label_name=label_name,
          weight_column_name=weight_column_name,
          head_name=head_name)
      self._thresholds = thresholds if thresholds else (.5,)
      self._loss_fn = loss_fn if loss_fn else _log_loss_with_two_classes
      self._enable_centered_bias = enable_centered_bias
function_tokens: def __init__ ( self , label_name = None , weight_column_name = None , enable_centered_bias = False , head_name = None , loss_fn = None , thresholds = None ) : super ( _BinaryLogisticHead , self ) . __init__ ( problem_type = constants . ProblemType . LOGISTIC_REGRESSION , logits_dimension = 1 , label_name = label_name , weight_column_name = weight_column_name , head_name = head_name ) self . _thresholds = thresholds if thresholds else ( .5 , ) self . _loss_fn = loss_fn if loss_fn else _log_loss_with_two_classes self . _enable_centered_bias = enable_centered_bias