// CHECK-DAG: %[[cst0:.*]] = stablehlo.constant dense<0xFF800000> : tensor<f32
  // CHECK-DAG: %[[cst1:.*]] = stablehlo.constant dense<0x7FC00000> : tensor<f32>
  // CHECK-DAG: %[[cst2:.*]] = stablehlo.constant dense<6.000000e+00> : tensor<f32>
  // CHECK-DAG: %[[cst3:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<f32>
  // CHECK-NOT: %[[q0:.*]] = "quantfork.qcast"(%[[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-LABEL: testDilate
func.func @testDilate(%arg0: tensor<3x4x5xf32>) -> tensor<5x7x9xf32> {
  // CHECK: "tfl.dilate"(%arg0, %cst, %cst_0)
  %cst = arith.constant dense<1> : tensor<3xi32>
  %cst_0 = arith.constant dense<-1.0> : tensor<f32>
  %0 = "tfl.dilate"(%arg0, %cst, %cst_0) : (tensor<3x4x5xf32>, tensor<3xi32>, tensor<f32>) -> tensor<5x7x9xf32>
  func.return %0 : tensor<5x7x9xf32>
  // CHECK: return %0 : tensor<5x7x9xf32>

  // 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-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>>

func.func @fuseScalarAddIntoConv2dHalf(%arg0: tensor<256x32x32x3xf16>, %arg1: tensor<16x3x3x3xf16>) -> tensor<256x8x7x16xf16> {
  %cst = arith.constant dense<1.5> : tensor<f16>
  %cst_0 = arith.constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]> : tensor<16xf16>

		}
	}

	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 = arith.constant dense<1.000000e+00> : tensor<256x256xf32>
  %cst_0 = arith.constant dense<0> : tensor<i32>
  %cst_1 = arith.constant dense<-1> : tensor<i32>

  %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)