message: Update losses.py
  Did the changes as per Mihai's comment
diff: @@ -1404,21 +1404,19 @@ def categorical_hinge(y_true, y_pred):
  where `neg=maximum((1-y_true)*y_pred) and pos=sum(y_true*y_pred)`
  Usage:
  - ```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.))
  - ```
  + >>> 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).
  + either `{-1, +1}` or `{0, 1}` (i.e. a one-hot-encoded tensor).
  y_pred: The predicted values.
  Returns:
  