diff --git a/tf_adapter/python/npu_bridge/estimator/npu_aicore_ops.py b/tf_adapter/python/npu_bridge/estimator/npu_aicore_ops.py
index 2098dd510e67f5a026119d789be04f1370179b44..bc5b8a42d15fb41f80be6c55f69eb127f7509f8b 100644
--- a/tf_adapter/python/npu_bridge/estimator/npu_aicore_ops.py
+++ b/tf_adapter/python/npu_bridge/estimator/npu_aicore_ops.py
@@ -125,6 +125,37 @@ def _DropOutDoMaskV3Grad(op, grad):
     return [result, None, None]
 
 
+def dropout_v4(x, keep_prob, noise_shape=None, seed=None, output_dtype=dtypes.bool, name=None):
+    """The gradient for `gelu`.
+
+    Args:
+        x: A tensor with type is float.
+        keep_prob: A tensor, float, rate of every element reserved.
+        noise_shape: A 1-D tensor, with type int32, shape of keep/drop what random
+            generated.
+        seed: Random seed.
+        output_dtype: dtype of output tensor, default is bool.
+        name: Layer name.
+
+    Returns:
+        A tensor.
+    """
+    x = ops.convert_to_tensor(x, name="x")
+    if not x.dtype.is_floating:
+        raise ValueError("x must be a floating point tensor."
+                         " Got a %s tensor instead." % x.dtype)
+    if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1.0:
+        raise ValueError("keep_prob must be a float value or a scalar tensor in the "
+                         "range (0, 1], got %g" % keep_prob)
+    if isinstance(keep_prob, float) and keep_prob == 1.0:
+        return x
+    seed, seed2 = random_seed.get_seed(seed)
+    noise_shape = _get_noise_shape(x, noise_shape)
+    gen_out = npu_aicore_ops.drop_out_gen_mask_v3(noise_shape, keep_prob, seed, seed2, output_dtype, name)
+    result = npu_aicore_ops.drop_out_do_mask_v3(x, gen_out, keep_prob, name)
+    return result
+
+
 def lru_cache_v2(index_list, data, cache, tag, is_last_call, pre_route_count, name=None):
     """
     LRUCacheV2 op