%0 = "tf.EmptyTensorList"(%arg0, %arg1) : (tensor<?xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi64>>>
  // CHECK: %cst = arith.constant dense<0> : tensor<i32>
  // CHECK: %0 = "tfl.custom"(%arg0, %cst) <{custom_code = "TensorListReserve", custom_option = #tfl<const_bytes : "0x04">}> : (tensor<?xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi64>>>

      %cst = "tf.Const"() {value = dense<[1, -1, 4]> : tensor<3xi32>} : () -> tensor<3xi32>

  // CHECK-DAG: [[UPDATE1:%.+]] = mhlo.dynamic_update_slice %arg0, [[SLICE4]], [[RESHAPE1]], [[CST]], [[CST]]
  // CHECK-DAG: [[UPDATE2:%.+]] = mhlo.dynamic_update_slice [[UPDATE1]], [[SLICE5]], [[RESHAPE2]], [[CST]], [[CST]]
  // CHECK-DAG: [[UPDATE3:%.+]] = mhlo.dynamic_update_slice [[UPDATE2]], [[SLICE6]], [[RESHAPE3]], [[CST]], [[CST]]

// CHECK-LABEL: testWhileRegionExternInCond
func.func @testWhileRegionExternInCond(%arg0 : tensor<*xf32>, %arg1 : tensor<i32>, %arg2 : tensor<i32>) -> tensor<*xf32> {
  %cst = arith.constant dense<4> : tensor<i32>
  %limit = "tf.Add"(%arg2, %cst) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  // CHECK: [[Result:%.*]]:3 = "tf.While"(%arg0, %arg1, %{{.+}} <{body = @tf.WhileRegion_body, cond = @tf.WhileRegion_cond

     }) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : () -> (tensor<!tf_type.string>, tensor<3x!tf_type.string>)
     "tf_device.launch"() ({
       %cst = "tf.Const"() {value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
       "tf.OpA"(%0#1) {mini_batch_splits = ""} : (tensor<3x!tf_type.string>) -> ()
       tf_device.return

// CHECK:         }

// -----

func.func @ensureBiasForConv2d(%arg0: tensor<128x32x32x3xf32>, %arg1: tensor<32x1x1x3xf32>) -> tensor<128x32x32x32xf32> {
  %cst = "tfl.no_value"() {value = unit} : () -> none

  ElementsAttr attr;
  if (auto cst = dyn_cast<mlir::arith::ConstantOp>(inst)) {
    // arith::ConstantOp have ElementAttr at this point due to validation of the
    // TFLite module.
    attr = mlir::cast<ElementsAttr>(cst.getValue());
  } else if (auto cst = dyn_cast<mlir::TF::ConstOp>(inst)) {
    attr = cst.getValue();
  } else if (auto cst = dyn_cast<tfl::ConstOp>(inst)) {
    attr = cst.getValue();

		// buffers all 11 bytes and then adds its own
		// Content-Length. To prevent the Server's
		// Content-Length and test ServeFile only, flush here.
		w.(Flusher).Flush()
	}))
	resp, err := cst.c.Get(cst.ts.URL)
	if err != nil {
		t.Fatal(err)
	}
	resp.Body.Close()
	if g, e := resp.ContentLength, int64(-1); g != e {
		t.Errorf("Content-Length mismatch: got %d, want %d", g, e)
	}
}

        builder.setInsertionPointAfter(cst.getOperation());
        auto new_op = builder.create<tfl::QConstOp>(
            cst.getLoc(), new_output_type, mlir::TypeAttr::get(new_output_type),
            cst.getValueAttr());
        full_range_const = new_op.getOutput();
      }
      use.set(full_range_const);
    }
    if (cst.use_empty()) cst.erase();
  });
  return func;
}

    module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
      func.func @main() -> (tensor<2xi32>) {
        %cst = "tf.Const"() {value = dense<[524170, 523952]> : tensor<2xi32>} : () -> tensor<2xi32>
        return %cst : tensor<2xi32>
    }
  })";

  auto compilation_result = CompileMlirModule(
      kHasReturnValuesAndNoMetadataRetvals,