diff --git a/keras_core/operations/core.py b/keras_core/operations/core.py
index 32b60b214..76a720f9c 100644
--- a/keras_core/operations/core.py
+++ b/keras_core/operations/core.py
@@ -34,43 +34,44 @@ class ScatterUpdate(Operation):
 
 @keras_core_export("keras_core.operations.scatter_update")
 def scatter_update(inputs, indices, updates):
-    """Update inputs by scattering updates at indices.
+    """Update inputs via updates at scattered (sparse) indices.
 
-    At a high level, this operation does `inputs[indices]=updates`. In details,
-    assume `inputs` is a tensor of shape `[D0, D1, ..., Dn]`, there are 2 main
+    At a high level, this operation does `inputs[indices] = updates`.
+    Assume `inputs` is a tensor of shape `(D0, D1, ..., Dn)`, there are 2 main
     usages of `scatter_update`.
 
-    - `indices` is a 2D tensor of shape `[num_updates, n]`, where `num_updates`
+    1. `indices` is a 2D tensor of shape `(num_updates, n)`, where `num_updates`
         is the number of updates to perform, and `updates` is a 1D tensor of
-        shape `[num_updates]`. For example, if `inputs = np.zeros([4, 4, 4])`,
+        shape `(num_updates,)`. For example, if `inputs` is `zeros((4, 4, 4))`,
         and we want to update `inputs[1, 2, 3]` and `inputs[0, 1, 3]` as 1, then
-        we can use
+        we can use:
 
-        ```
-        inputs = np.zeros([4, 4, 4])
-        indices = [[1, 2, 3], [0, 1, 3]]
-        updates = np.array([1., 1.])
-        inputs = keras_core.operations.scatter_update(inputs, indices, updates)
-        ```
-    - `indices` is a 2D tensor of shape `[num_updates, k]`, where `num_updates`
+    ```python
+    inputs = np.zeros((4, 4, 4))
+    indices = [[1, 2, 3], [0, 1, 3]]
+    updates = np.array([1., 1.])
+    inputs = keras_core.operations.scatter_update(inputs, indices, updates)
+    ```
+
+    2 `indices` is a 2D tensor of shape `(num_updates, k)`, where `num_updates`
         is the number of updates to perform, and `k` (`k < n`) is the size of
-        each index in `indices`. `updates` is a `n-k`-D tensor of shape
-        `[num_updates, inputs.shape[k:]]`. For example, if
-        `inputs = np.zeros([4, 4, 4])`, and we want to update `inputs[1, 2, :]`
+        each index in `indices`. `updates` is a `n - k`-D tensor of shape
+        `(num_updates, inputs.shape[k:])`. For example, if
+        `inputs = np.zeros((4, 4, 4))`, and we want to update `inputs[1, 2, :]`
         and `inputs[2, 3, :]` as `[1, 1, 1, 1]`, then `indices` would have shape
-        `[num_updates, 2]` (`k=2`), and `updates` would have shape
-        `[num_updates, 4]` (`inputs.shape[2:]=4`). See the code below:
+        `(num_updates, 2)` (`k = 2`), and `updates` would have shape
+        `(num_updates, 4)` (`inputs.shape[2:] = 4`). See the code below:
 
-        ```
-        inputs = np.zeros([4, 4, 4])
-        indices = [[1, 2], [2, 3]]
-        updates = np.array([[1., 1., 1, 1,], [1., 1., 1, 1,])
-        inputs = keras_core.operations.scatter_update(inputs, indices, updates)
-        ```
+    ```python
+    inputs = np.zeros((4, 4, 4))
+    indices = [[1, 2], [2, 3]]
+    updates = np.array([[1., 1., 1, 1,], [1., 1., 1, 1,])
+    inputs = keras_core.operations.scatter_update(inputs, indices, updates)
+    ```
 
     Args:
         inputs: A tensor, the tensor to be updated.
-        indices: A tensor or list/tuple of shape `[N, inputs.ndims]`, specifying
+        indices: A tensor or list/tuple of shape `(N, inputs.ndim)`, specifying
             indices to update. `N` is the number of indices to update, must be
             equal to the first dimension of `updates`.
         updates: A tensor, the new values to be put to `inputs` at `indices`.
@@ -96,25 +97,25 @@ def block_update(inputs, start_indices, updates):
     """Update inputs block.
 
     At a high level, this operation does
-    `inputs[start_indices: start_indices + updates.shape] = updates`. In
-    details, assume inputs is a tensor of shape `[D0, D1, ..., Dn]`,
+    `inputs[start_indices: start_indices + updates.shape] = updates`.
+    Assume inputs is a tensor of shape `(D0, D1, ..., Dn)`,
     `start_indices` must be a list/tuple of n integers, specifying the starting
     indices. `updates` must have the same rank as `inputs`, and the size of each
     dim must not exceed `Di - start_indices[i]`. For example, if we have 2D
-    inputs `inputs=np.zeros([5, 5])`, and we want to update the intersection of
-    last 2 rows and last 2 columns as 1, i.e.,
-    `inputs[3:, 3:] = np.ones([2, 2])`, then we can use the code below:
+    inputs `inputs = np.zeros((5, 5))`, and we want to update the intersection
+    of last 2 rows and last 2 columns as 1, i.e.,
+    `inputs[3:, 3:] = np.ones((2, 2))`, then we can use the code below:
 
-    ```
-    inputs = np.zeros([5, 5])
+    ```python
+    inputs = np.zeros((5, 5))
     start_indices = [3, 3]
-    updates = np.ones([2, 2])
+    updates = np.ones((2, 2))
     inputs = keras_core.operations.block_update(inputs, start_indices, updates)
     ```
 
     Args:
         inputs: A tensor, the tensor to be updated.
-        start_indices: A list/tuple of shape `[inputs.ndims]`, specifying
+        start_indices: A list/tuple of shape `(inputs.ndim,)`, specifying
             the starting indices for updating.
         updates: A tensor, the new values to be put to `inputs` at `indices`.
             `updates` must have the same rank as `inputs`.