message: Update docstring for consine_similarity
  The edited docstring correctly indicates the `y_true` and `y_pred` need not be normalized, and that passing a zero vector as one of the arguments will return 0 regardless of the proximity between true and predicted labels.
diff: @@ -1216,9 +1216,11 @@ def cosine_similarity(y_true, y_pred, axis=-1):
  Note that it is a negative quantity between -1 and 0, where 0 indicates
  orthogonality and values closer to -1 indicate greater similarity. This makes
  it usable as a loss function in a setting where you try to maximize the
  - proximity between predictions and targets.
  + proximity between predictions and targets. If either `y_true` or `y_pred`
  + is a zero vector, cosine similarity will be 0 regardless of the proximity
  + between predictions and targets.
  - `loss = -sum(y_true * y_pred)`
  + `loss = -sum(l2_norm(y_true) * l2_norm(y_pred))`
  Args:
  y_true: Tensor of true targets.
  