%cst0 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<1x4xf32>} : () -> tensor<1x4xf32> loc("Const")
  %cst1 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<1x4xf32>} : () -> tensor<1x4xf32> loc("Const")

    attributes {tf.entry_function = {outputs = "result"}} {
  %cst0 = arith.constant dense<5> : tensor<i32> loc("N")
  %cst1 = arith.constant dense<3.0> : tensor<1xf32> loc("val")

  %0:2 = "tfl.while"(%cst0, %cst1) ({
    ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
      %cst_0 = arith.constant dense<0> : tensor<i32>
      %1 = "tfl.greater"(%arg2, %cst_0) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>

    // CHECK: %[[CST0:.*]] = stablehlo.constant dense<0xFF800000> : tensor<f32>
    // CHECK: %[[CST1:.*]] = stablehlo.constant dense<0xFF80000E> : tensor<2x3x1024x3xf32>
    // CHECK: %[[Q0:.*]] = "quantfork.qcast"(%[[CST0]])
    // CHECK: %[[Q1:.*]] = "quantfork.qcast"(%[[CST1]])
    // CHECK: %[[Q2:.*]] = "quantfork.qcast"(%[[ARG0]])

// CHECK: %[[cst0:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<32x!quant.uniform<i32:f32, 7.812500e-04>>, value = dense<-1583> : tensor<32xi32>}>
// CHECK: %[[cst1:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 1.000000e-01>>, value = dense<1> : tensor<32x3x3x3xi8>}>
// CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[cst1]], %[[cst0]])

  // CHECK-DAG: [[cst6:%.*]] = arith.constant dense<4.000000e+00> : tensor<f32>
  // CHECK: return [[cst0]], [[cst1]], [[cst2]], [[cst3]], [[cst4]], [[cst5]], [[cst6]]

  %7 = "tfl.abs"(%0) : (tensor<f32>) -> tensor<f32>
  %8 = "tfl.sin"(%1) : (tensor<f32>) -> tensor<f32>
  %9 = "tfl.cos"(%2) : (tensor<f32>) -> tensor<f32>
  %10 = "tfl.log"(%3) : (tensor<f32>) -> tensor<f32>

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
  func.func @main() -> () {
    %cst0 = "tf.Const"(){ value = dense<0> : tensor<3x5xi1>} : () -> tensor<3x5xi1>
    %0 = "tf.Where"(%cst0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
    func.return
  }
})";

absl::StatusOr<XlaCompiler::CompilationResult> CompileMlirModule(
    const char* mlir_module_str,

// CHECK-NEXT: %[[cst1:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<32x3x3x3x!quant.uniform<i8<-127:127>:f32, 0.021826678373682216:151>>, value = dense<-76> : tensor<32x3x3x3xi8>}> : () -> tensor<32x3x3x3x!quant.uniform<i8<-127:127>:f32, 0.021826678373682216>>

		}
	}

	if cst.ts.Config.ErrorLog == nil {
		cst.ts.Config.ErrorLog = log.New(testLogWriter{t}, "", 0)
	}

	switch mode {
	case http1Mode:
		cst.ts.Start()
	case https1Mode:
		cst.ts.StartTLS()
	case http2Mode:
		ExportHttp2ConfigureServer(cst.ts.Config, nil)
		cst.ts.TLS = cst.ts.Config.TLSConfig
		cst.ts.StartTLS()
	default:
		t.Fatalf("unknown test mode %v", mode)
	}
	cst.c = cst.ts.Client()

  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 5, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  func.return %1 : tensor<2x1xf32>

// CHECK:  %cst = arith.constant
// CHECK:  %0 = "tf.Reshape"(%arg0, %cst)

  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  func.return %1 : tensor<2x1xf32>

// CHECK:  %cst = arith.constant
// CHECK:  %0 = "tf.Reshape"(%arg0, %cst)