}
		}

		io.WriteString(w, r.RemoteAddr)
	})

	cst = newClientServerTest(t, mode, handler, func(srv *httptest.Server) {
		srv.Config.RegisterOnShutdown(func() { close(gotOnShutdown) })
	})

	out := get(t, cst.c, cst.ts.URL) // calls t.Fail on failure
	t.Logf("%v: %q", cst.ts.URL, out)

	if err := <-shutdownRes; err != nil {

  %cst = arith.constant dense<[1, 2]> : tensor<2xi64>
  %0 = "tf.Reshape"(%arg0, %cst) : (tensor<*xf32>, tensor<2xi64>) -> tensor<1x2xf32>
  func.return %0 : tensor<1x2xf32>

// CHECK-LABEL: dynamicReshapeI64Fold
// CHECK:  %[[cst:.*]] = arith.constant dense<[1, 2]> : tensor<2xi32>
// CHECK-NEXT:  %[[reshape:.*]] = "tfl.reshape"(%arg0, %[[cst]]) : (tensor<*xf32>, tensor<2xi32>) -> tensor<1x2xf32>

  %cst = "tfl.no_value"() {value = unit} : () -> none

				timeout *= 2
				retry = true
				cst.close()
			}
			return true
		}

		if _, err := cst.c.Get(cst.ts.URL); err != nil {
			if tooShort(err) {
				continue
			}
			t.Fatalf("got error: %s", err)
		}

		time.Sleep(10 * timeout)
		if _, err := cst.c.Get(cst.ts.URL); err != nil {
			if tooShort(err) {
				continue
			}

  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
  // CHECK: %[[RES0:.*]] = "tf.GatherV2"(%arg0, %arg2, %cst) <{batch_dims = 0 : i64}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<1600x1x32xf32>, tensor<i32>, tensor<i32>) -> tensor<1x32xf32>

  %cst = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %0:2 = "tf.Split"(%cst, %input) : (tensor<i32>, tensor<*xf32>) -> (tensor<*xf32>, tensor<*xf32>)
  func.return
}

func.func @testSplitUnknownDimInput(%input: tensor<4x?x4xf32>) {
  %cst = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

  }
  // CHECK-LABEL: unppack_const_quant() -> tensor<!quant.uniform<u8:f32, 7.000000e-03:128>>
  func.func @unppack_const_quant() -> (tensor<*x!quant.uniform<u8:f32, 0.007:128>>) {
    %cst = arith.constant dense<5> : tensor<2xi8>
    %0 = "quant.scast"(%cst) : (tensor<2xi8>) -> tensor<2x!quant.uniform<u8:f32, 0.007:128>>
    // CHECK: (tensor<*x!quant.uniform<u8:f32, 7.000000e-03:128>>, tensor<!quant.uniform<u8:f32, 7.000000e-03:128>>)

    // Only fuse multiplier if all dimensions other than the depth dimension
    // are equal to 1 since otherwise
    // `matmul(x, filter) * cst != matmul(x, filter * cst)`
    // even if `filter` and `cst` are be broadcastable.
    auto shape = cst.getType().getShape();
    if (!IsDimensionsDegenerateExceptLastOne(shape)) return failure();

    int64_t element_size = shape.empty() ? 1 : shape[shape.size() - 1];

  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();

  LogicalResult matchAndRewrite(AssertOp op,
                                PatternRewriter& rewriter) const override {
    ElementsAttr cst;
    if (matchPattern(op.getCondition(), m_Constant(&cst))) {
      if (cst.getValues<bool>()[0]) {
        rewriter.eraseOp(op);
        return success();
      }
    }
    return failure();
  }
};
}  // namespace