parameters: (n_classes,
                       weight_column=None,
                       label_vocabulary=None,
                       name=None)
return_statement: return head_lib._multi_class_head_with_softmax_cross_entropy_loss(  # pylint:disable=protected-access
        n_classes=n_classes,
        weight_column=weight_column,
        label_vocabulary=label_vocabulary,
        name=name)
docstring: Creates a `_Head` for multi class classification.
  
    Uses `sparse_softmax_cross_entropy` loss.
  
    This head expects to be fed integer labels specifying the class index.
  
    Args:
      n_classes: Number of classes, must be greater than 2 (for 2 classes, use
        `binary_classification_head`).
      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.
      label_vocabulary: A list of strings represents possible label values. If it
        is not given, that means labels are already encoded as integer within
        [0, n_classes). If given, labels must be string type and have any value in
        `label_vocabulary`. Also there will be errors if vocabulary is not
        provided and labels are string.
      name: name of the head. If provided, summary and metrics keys will be
        suffixed by `"/" + name`. Also used as `name_scope` when creating ops.
  
    Returns:
      An instance of `_Head` for multi class classification.
  
    Raises:
      ValueError: if `n_classes`, `metric_class_ids` or `label_keys` is invalid.
docstring_summary: Creates a `_Head` for multi class classification.
docstring_tokens: Creates a _Head for multi class classification .
function: def multi_class_head(n_classes,
                       weight_column=None,
                       label_vocabulary=None,
                       name=None):
    """Creates a `_Head` for multi class classification.
  
    Uses `sparse_softmax_cross_entropy` loss.
  
    This head expects to be fed integer labels specifying the class index.
  
    Args:
      n_classes: Number of classes, must be greater than 2 (for 2 classes, use
        `binary_classification_head`).
      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.
      label_vocabulary: A list of strings represents possible label values. If it
        is not given, that means labels are already encoded as integer within
        [0, n_classes). If given, labels must be string type and have any value in
        `label_vocabulary`. Also there will be errors if vocabulary is not
        provided and labels are string.
      name: name of the head. If provided, summary and metrics keys will be
        suffixed by `"/" + name`. Also used as `name_scope` when creating ops.
  
    Returns:
      An instance of `_Head` for multi class classification.
  
    Raises:
      ValueError: if `n_classes`, `metric_class_ids` or `label_keys` is invalid.
    """
    return head_lib._multi_class_head_with_softmax_cross_entropy_loss(  # pylint:disable=protected-access
        n_classes=n_classes,
        weight_column=weight_column,
        label_vocabulary=label_vocabulary,
        name=name)
function_tokens: def multi_class_head ( n_classes , weight_column = None , label_vocabulary = None , name = None ) : return head_lib . _multi_class_head_with_softmax_cross_entropy_loss ( # pylint:disable=protected-access n_classes = n_classes , weight_column = weight_column , label_vocabulary = label_vocabulary , name = name )