diff --git a/caffe2/operators/gather_ranges_to_dense_op.cc b/caffe2/operators/gather_ranges_to_dense_op.cc
index 10396aafc97..aa31ef12b36 100644
--- a/caffe2/operators/gather_ranges_to_dense_op.cc
+++ b/caffe2/operators/gather_ranges_to_dense_op.cc
@@ -104,3 +104,11 @@ NO_GRADIENT(GatherRangesToDense);
 
 } // namespace
 } // namespace caffe2
+
+using GatherRangesToDenseCPUOp =
+    caffe2::GatherRangesToDenseOp<caffe2::CPUContext>;
+
+C10_EXPORT_CAFFE2_OP_TO_C10_CPU(
+    GatherRangesToDense,
+    "_caffe2::GatherRangesToDense(Tensor data, Tensor ranges, Tensor? key, int[] lengths, int min_observation, float max_mismatched_ratio, float max_empty_ratio) -> Tensor[] outputs",
+    GatherRangesToDenseCPUOp);
diff --git a/caffe2/operators/gather_ranges_to_dense_op.h b/caffe2/operators/gather_ranges_to_dense_op.h
index c1dd5a52700..217a61b2512 100644
--- a/caffe2/operators/gather_ranges_to_dense_op.h
+++ b/caffe2/operators/gather_ranges_to_dense_op.h
@@ -5,6 +5,7 @@
 
 #include "caffe2/core/common_omp.h"
 #include "caffe2/core/context.h"
+#include "caffe2/core/export_caffe2_op_to_c10.h"
 #include "caffe2/core/logging.h"
 #include "caffe2/core/operator.h"
 #include "caffe2/core/types.h"
@@ -15,6 +16,8 @@
 #include <map>
 #include <utility>
 
+C10_DECLARE_EXPORT_CAFFE2_OP_TO_C10(GatherRangesToDense);
+
 namespace caffe2 {
 template <class Context>
 class GatherRangesToDenseOp final : public Operator<Context> {
diff --git a/caffe2/python/operator_test/torch_integration_test.py b/caffe2/python/operator_test/torch_integration_test.py
index 55f26a89987..9bec6476424 100644
--- a/caffe2/python/operator_test/torch_integration_test.py
+++ b/caffe2/python/operator_test/torch_integration_test.py
@@ -875,6 +875,47 @@ class TorchIntegration(hu.HypothesisTestCase):
         )
         torch.testing.assert_allclose(expected_output, actual_output.cpu())
 
+    def test_gather_ranges_to_dense_op(self):
+        data = np.array([1, 2, 3, 4, 5, 6, 7, 8])
+        ranges = np.array([[[2, 4]], [[0, 0]]])
+        key = np.array([0, 1, 3, 2, 1, 0, 1, 0])
+        lengths = np.array([4])
+        min_observation = 2
+        max_mismatched_ratio = 0.5
+        max_empty_ratio = 1.0
+
+        outputs_name = ["X_{}".format(i) for i in range(len(lengths))]
+        ref_op = core.CreateOperator(
+            "GatherRangesToDense",
+            ["data", "ranges", "key"],
+            outputs_name,
+            lengths=lengths,
+            min_observation=min_observation,
+            max_mismatched_ratio=max_mismatched_ratio,
+            max_empty_ratio=max_empty_ratio,
+        )
+        workspace.FeedBlob("data", data)
+        workspace.FeedBlob("ranges", ranges)
+        workspace.FeedBlob("key", key)
+        workspace.RunOperatorOnce(ref_op)
+        ref_outputs = []
+        for output_name in outputs_name:
+            ref_outputs.append(workspace.FetchBlob(output_name))
+
+        outputs = torch.ops._caffe2.GatherRangesToDense(
+            torch.from_numpy(data),
+            torch.from_numpy(ranges),
+            torch.from_numpy(key),
+            lengths=lengths,
+            min_observation=min_observation,
+            max_mismatched_ratio=max_mismatched_ratio,
+            max_empty_ratio=max_empty_ratio,
+        )
+
+        self.assertEqual(len(ref_outputs), len(outputs))
+        for i in range(0, len(ref_outputs)):
+            np.testing.assert_array_almost_equal(ref_outputs[i], outputs[i].numpy())
+
     @given(lengths_0=st.integers(1, 10), lengths_1=st.integers(1, 10))
     @settings(deadline=1000)
     def test_merge_id_lists(self, lengths_0, lengths_1):