code: def hamming_loss_fn(
      y_true: TensorLike,
      y_pred: TensorLike,
      threshold: Union[FloatTensorLike, None],
      mode: str,
  ) -> tf.Tensor:
      """Computes hamming loss.
  
      Hamming loss is the fraction of wrong labels to the total number
      of labels.
  
      In multi-class classification, hamming loss is calculated as the
      hamming distance between `y_true` and `y_pred`.
      In multi-label classification, hamming loss penalizes only the
      individual labels.
  
      Args:
          y_true: actual target value.
          y_pred: predicted target value.
          threshold: Elements of `y_pred` greater than threshold are
              converted to be 1, and the rest 0. If threshold is
              None, the argmax is converted to 1, and the rest 0.
          mode: multi-class or multi-label.
  
      Returns:
          hamming loss: float.
      """
      if mode not in ["multiclass", "multilabel"]:
          raise TypeError(
              "mode must be either multiclass or multilabel]"
          )
  
      if threshold is None:
          threshold = tf.reduce_max(y_pred, axis=-1, keepdims=True)
          # make sure [0, 0, 0] doesn't become [1, 1, 1]
          # Use abs(x) > eps, instead of x != 0 to check for zero
          y_pred = tf.logical_and(
              y_pred >= threshold, tf.abs(y_pred) > 1e-12
          )
      else:
          y_pred = y_pred > threshold
  
      y_true = tf.cast(y_true, tf.int32)
      y_pred = tf.cast(y_pred, tf.int32)
  
      if mode == "multiclass":
          nonzero = tf.cast(
              tf.math.count_nonzero(y_true * y_pred, axis=-1),
              tf.float32,
          )
          return 1.0 - nonzero
  
      else:
          nonzero = tf.cast(
              tf.math.count_nonzero(y_true - y_pred, axis=-1),
              tf.float32,
          )
          return nonzero / y_true.get_shape()[-1]
  
file_path: addons/tensorflow_addons/metrics/hamming.py
imports: import tensorflow
  from tensorflow_addons.metrics.utils import MeanMetricWrapper
  from tensorflow_addons.utils.types import FloatTensorLike, TensorLike, AcceptableDTypes
  from typeguard import typechecked
  from typing import Union, Optional
label_desc: Use image node_id 2 for calling a global function with example usage: hamming_loss_fn(y_true, y_pred, threshold, mode) and returns: unknown, unknown