message: Update losses.py
  Added Backticks as per suggestion
diff: @@ -1390,16 +1390,16 @@ def categorical_hinge(y_true, y_pred):
  where `neg=maximum((1-y_true)*y_pred) and pos=sum(y_true*y_pred)`
  Usage:
  -
  - >>> y_true = np.random.randint(0, 3, size=(2,))
  - >>> y_true = tf.keras.utils.to_categorical(y_true, num_classes=3)
  - >>> y_pred = np.random.random(size=(2, 3))
  - >>> loss = tf.keras.losses.categorical_hinge(y_true, y_pred)
  - >>> assert loss.shape == (2,)
  - >>> pos = np.sum(y_true * y_pred, axis=-1)
  - >>> neg = np.amax((1. - y_true) * y_pred, axis=-1)
  - >>> assert np.array_equal(loss.numpy(), np.maximum(0., neg - pos + 1.))
  -
  + ```python
  + y_true = np.random.randint(0, 3, size=(2,))
  + y_true = tf.keras.utils.to_categorical(y_true, num_classes=3)
  + y_pred = np.random.random(size=(2, 3))
  + loss = tf.keras.losses.categorical_hinge(y_true, y_pred)
  + assert loss.shape == (2,)
  + pos = np.sum(y_true * y_pred, axis=-1)
  + neg = np.amax((1. - y_true) * y_pred, axis=-1)
  + assert np.array_equal(loss.numpy(), np.maximum(0., neg - pos + 1.))
  + ```
  Args:
  y_true: The ground truth values. `y_true` values are expected to be
  either {-1, +1} or {0, 1}(i.e. a one-hot-encoded tensor).
  