}
		}

		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 {

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s

// CHECK: %[[CST:.*]] = "tfl.no_value"() <{value}> : () -> none
// CHECK: %[[RES0:.*]] = "tfl.conv_2d"(%arg0, %arg1, %[[CST]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, none) -> tensor<256x32x32x16xf32>

    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island {
        %cst = "tf.Const"() <{value = dense<false> : tensor<i1>}> : () -> tensor<*xi1>
        tf_executor.yield %cst : tensor<*xi1>
      }
      tf_executor.fetch %outputs : tensor<*xi1>
    }
    return %0 : tensor<*xi1>
  }

  func.return %2 : tensor<256x8x7x16xf32>

// CHECK-DAG: %[[cst:.*]] = "tf.Const{{.*}} dense<8.000000e+00> : tensor<3x3x3x16xf32>
// CHECK-DAG: %[[cst_0:.*]] = "tf.Const{{.*}} dense<1.200000e+01> : tensor<16xf32>
// CHECK-NEXT: %[[conv:.*]] = "tf.Conv2D"(%arg0, %[[cst]])
// CHECK-NEXT: %[[bias:.*]] = "tf.AddV2"(%[[conv]], %[[cst_0]])
// CHECK-NEXT: return %[[bias]] : tensor<256x8x7x16xf32>
}

    %1 = func.call @WhileOp_cond(%arg0, %arg1, %cst) : (tensor<*xi32>, tensor<*xf32>, tensor<i32>) -> tensor<i1>
    "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>):
    // CHECK: call @WhileOp_body
    // CHECK-SAME: (tensor<*xi32>, tensor<*xf32>, tensor<i32>)

  return %1 : tensor<1x3xf32>
}
// CHECK: @main(%[[ARG_0:.+]]: tensor<1x4xf32>) -> tensor<1x3xf32>
// CHECK-DAG: %[[CST:.+]] = stablehlo.constant dense<1.000000e+00> : tensor<4x3xf32>

  func.return %3 : tensor<2x1xf32>
}

// CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<4xi32>
// CHECK:  [[VAL_0:%.*]] = "tfl.reshape"(%1, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
// CHECK:  [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>

    func.func @main(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> {
      %cst = "tf.Const"() {value = dense<3.00000000e-1> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
      %0 = "quantfork.stats"(%arg0) {layerStats = dense<[6.00000000e-6, 9.00000000e-1]> : tensor<2xf32>} : (tensor<1x2xf32>) -> tensor<1x2xf32>

  func.return %2 : tensor<2x2xf32>

// CHECK:  %[[cst:.*]] = "tfl.pseudo_const"(){{.*}}dense<-1.000000e-01> : tensor<2x2xf32>
// CHECK:  return %[[cst]] : tensor<2x2xf32>
}

// CHECK-LABEL: DequantizeAndQuantize
func.func @DequantizeAndQuantize() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {

func.func @main(tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32> {
^bb0(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>):
  %cst = arith.constant dense<1.0> : tensor<40xf32>
  %0:2 = "tfl.fully_connected"(%arg0, %arg1, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<40x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> (tensor<40x40xf32>, tensor<40x40xf32>)