update comments

Author: Jerry-Jzy

deepxde/gradients/gradients.py

@@ -21,7 +21,7 @@ def jacobian(ys, xs, i=None, j=None):
     Args:
         ys: Output Tensor of shape (batch_size, dim_y) or (batch_size_out, batch_size,
             dim_y). Here, the `batch_size` is the same one for `xs`, and
-            `batch_size_out` is the batch size for an additional dimension.
+            `batch_size_out` is the batch size for an additional/outer dimension.
         xs: Input Tensor of shape (batch_size, dim_x).
         i (int or None): `i`th row. If `i` is ``None``, returns the `j`th column
             J[:, `j`].
@@ -52,7 +52,7 @@ def hessian(ys, xs, component=0, i=0, j=0):
     Args:
         ys: Output Tensor of shape (batch_size, dim_y) or (batch_size_out, batch_size,
             dim_y). Here, the `batch_size` is the same one for `xs`, and
-            `batch_size_out` is the batch size for an additional dimension.
+            `batch_size_out` is the batch size for an additional/outer dimension.
         xs: Input Tensor of shape (batch_size, dim_x).
         component: `ys[:, component]` is used as y to compute the Hessian.
         i (int): `i`th row.