stats[arn] = &BucketReplicationStat{
				Failed:          fstats.toMetric(),
				FailStats:       fstats,
				ReplicatedSize:  stat.ReplicatedSize + oldst.ReplicatedSize,
				ReplicatedCount: stat.ReplicatedCount + oldst.ReplicatedCount,
				Latency:         stat.Latency.merge(oldst.Latency),
				XferRateLrg:     &lrg,
				XferRateSml:     &sml,
			}
			totReplicatedSize += stat.ReplicatedSize

  status->status = c.construction_status();
  if (!status->status.ok()) return;

  if (op_reg_data->shape_inference_fn == nullptr) {
    status->status =
        InvalidArgument("No shape inference function exists for op '",
                        node_def.op(), "', did you forget to define it?");
    return;
  }

  status->status = c.Run(op_reg_data->shape_inference_fn);

      TF_NewStatus(), TF_DeleteStatus);
  int assign = TF_OpIsStateful("AssignAddVariableOp", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  EXPECT_EQ(assign, 1);
  int id = TF_OpIsStateful("Identity", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  EXPECT_EQ(id, 0);
}

class ShapeInferenceTest : public ::testing::Test {
 protected:

xmlns="http://s3.amazonaws.com/doc/2006-03-01/"><Role></Role><Rule><Status>Enabled</Status><DeleteMarkerReplication><Status>Disabled</Status></DeleteMarkerReplication><DeleteReplication><Status>Disabled</Status></DeleteReplication><Prefix>key-prefix</Prefix><Destination><Bucket>arn:minio:replication:::destinationbucket</Bucket></Destination></Rule><Rule><Status>Enabled</Status><Priority>3</Priority><DeleteMarkerReplication><Status>Disabled</Status></DeleteMarkerReplication><DeleteReplication><Status>Disab...

  if (TF_GetCode(status) != TF_OK) return;
  if (!grad) return;

  status->status = g->graph.AddFunctionDef(grad->record->fdef(),
                                           grad->record->stack_traces());
  if (TF_GetCode(status) != TF_OK) return;

  tensorflow::GradientDef gdef;
  gdef.set_function_name(func->record->fdef().signature().name());
  gdef.set_gradient_func(grad->record->fdef().signature().name());

replication also cycles through `PENDING` to `COMPLETED` or `FAILED` states for the `X-Amz-Replication-Status` on the source when a delete marker is set (i.e. performing `mc rm` on an object without specifying a version).After replication syncs the delete marker on the target, the DeleteMarker on the target shows `X-Amz-Replication-Status` of `REPLICA`. The status of DeleteMarker replication is returned by `X-Minio-Replication-DeleteMarker-Status` header on `HEAD/GET` calls for the delete marker version in...

		Rules: []replication.Rule{
			{
				Status:                    replication.Enabled,
				Priority:                  1,
				DeleteMarkerReplication:   replication.DeleteMarkerReplication{Status: replication.Enabled},
				DeleteReplication:         replication.DeleteReplication{Status: replication.Enabled},
				Filter:                    replication.Filter{},

//
// On success, returns the newly created library handle and places OK in status.
// The caller owns the library handle.
//
// On failure, returns nullptr and places an error status in status.
TF_CAPI_EXPORT extern TF_Library* TF_LoadPluggableDeviceLibrary(
    const char* library_filename, TF_Status* status);

// Frees the memory associated with the library handle.
// Does NOT unload the library.

[![Fuzzing Status](https://oss-fuzz-build-logs.storage.googleapis.com/badges/tensorflow.svg)](https://bugs.chromium.org/p/oss-fuzz/issues/list?sort=-opened&can=1&q=proj:tensorflow)

			wantStatusCode: http.StatusOK,
			wantResp:       []string{"kms"},
		},

		// Status tests
		{
			name:   "status as root want success",
			method: http.MethodGet,
			path:   kmsStatusPath,
			asRoot: true,

			wantStatusCode: http.StatusOK,
			wantResp:       []string{"MinIO builtin"},
		},
		{
			name:   "status as user with no policy want forbidden",