Merge pull request #28816 from tensorflow:jvishnuvardhan-patch-6

PiperOrigin-RevId: 249097006

tensorflow/python/eager/backprop.py

@@ -993,7 +993,7 @@ def jacobian(self,
                experimental_use_pfor=True):
     """Computes the jacobian using operations recorded in context of this tape.
 
-    See http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant for the
+    See [wikipedia article](http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant) for the
     definition of a Jacobian.
 
     Example usage:
@@ -1094,15 +1094,16 @@ def batch_jacobian(self,
                      experimental_use_pfor=True):
     """Computes and stacks per-example jacobians.
 
-    See http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant for the
-    definition of a Jacobian.  This function is essentially an efficient
+    See [wikipedia article](http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant) for the
+    definition of a Jacobian. This function is essentially an efficient
     implementation of the following:
+    
     `tf.stack([self.jacobian(y[i], x[i]) for i in range(x.shape[0])])`.
 
     Note that compared to `GradientTape.jacobian` which computes gradient of
     each output value w.r.t each input value, this function is useful when
-    `target[i,...] is independent of `source[j,...]` for `j != i`. This
-    independence assumption allows more efficient computation as compared to
+    `target[i,...]` is independent of `source[j,...]` for `j != i`. This
+    assumption allows more efficient computation as compared to
     `GradientTape.jacobian`. The output, as well as intermediate activations,
     are lower dimensional and avoid a bunch of redundant zeros which would
     result in the jacobian computation given the independence assumption.
@@ -1111,10 +1112,10 @@ def batch_jacobian(self,
 
     ```python
     with tf.GradientTape() as g:
-      x = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
+      x = tf.constant([[1., 2.], [3., 4.]], dtype=tf.float32)
       g.watch(x)
       y = x * x
-    batch_jacobian = g.batch_jacobian(y, x)
+    batch_jacobian = g.batch_jacobian(y, x) 
     # batch_jacobian is [[[2,  0], [0,  4]], [[6,  0], [0,  8]]]
     ```