// CHECK-NOT: "tfl.batch_matmul"
func.func @Batchmatmul2Fullyconnected(%arg0: tensor<4x128x2xf32>) -> (tensor<4x128x1xf32>) {
  %0 = arith.constant dense<[[1.0], [2.0]]> : tensor<2x1xf32>
  %1 = "tfl.batch_matmul"(%arg0, %0) {adj_x = false, adj_y = false, asymmetric_quantize_inputs = false} : (tensor<4x128x2xf32>, tensor<2x1xf32>) -> tensor<4x128x1xf32>
  func.return %1 : tensor<4x128x1xf32>
  // CHECK-NEXT: %[[CONST_WEIGHT:.*]] = arith.constant

// CHECK-LABEL: QuantizeUnidirectionalLstmFullPerTensor
func.func @QuantizeUnidirectionalLstmFullPerTensor(%arg0: tensor<1x2x3xf32>) -> (tensor<1x2x3xf32>) {
  %input = "quantfork.stats"(%arg0) {layerStats = dense<[0.0, 1.0]> : tensor<2xf32>} : (tensor<1x2x3xf32>) -> tensor<1x2x3xf32>
  %1 = "tfl.pseudo_const"() {value = dense<[[0.1]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>

// CHECK: %[[RESULT:.*]] = "tfl.reshape"(%arg0, %[[CONST:.*]]) : (tensor<?x1x8x3xf32>, tensor<3xi32>) -> tensor<?x8x3xf32>
// CHECK:  return %[[RESULT]]
}

func.func @ConvertSqueezeToReshapeWithDynamicDimension2(%arg0: tensor<?x1x8x3xf32>) -> tensor<1x8x3xf32> {
  %0 = "tfl.squeeze"(%arg0) {squeeze_dims = [0]}: (tensor<?x1x8x3xf32>) -> tensor<1x8x3xf32>
  func.return %0: tensor<1x8x3xf32>

func.func @Int64SliceBeginSize(%arg0: tensor<4x128x32xf32>) -> tensor<1x128x32xf32> {
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi64>} : () -> tensor<3xi64>
  %1 = "tfl.pseudo_const"() {value = dense<[1, 128, 32]> : tensor<3xi64>} : () -> tensor<3xi64>
  %2 = "tfl.slice"(%arg0, %0, %1) : (tensor<4x128x32xf32>, tensor<3xi64>, tensor<3xi64>) -> tensor<1x128x32xf32>
  func.return %2 : tensor<1x128x32xf32>

  return %0 : tensor<1x1x2x2xf32>
}
func.func private @XlaCallModule_aten.avg_pool2d.default.impl_4(%arg0: tensor<1x1x3x3xf32>) -> tensor<1x1x2x2xf32>

// CHECK-LABEL: avg_pool2d_5
// CHECK: %cst = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>
// CHECK: %0 = "tfl.transpose"(%arg0, %cst) : (tensor<1x1x3x3xf32>, tensor<4xi32>) -> tensor<1x3x3x1xf32>

  func.return %17 : tensor<1x2x3xf32>

  // CHECK: %[[NONE:.*]] = "tfl.no_value"() <{value}> : () -> none
  // CHECK: %[[DQ_1:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_2:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_3:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>

  %3 = "tfl.quantize"(%2) {qtype = tensor<1x1x1x3x!quant.uniform<i8:f32, 0.1>>, volatile} : (tensor<1x1x1x3xf32>) -> tensor<1x1x1x3x!quant.uniform<i8:f32, 0.1>>
  %4 = "tfl.dequantize"(%3) : (tensor<1x1x1x3x!quant.uniform<i8:f32, 0.1>>) -> tensor<1x1x1x3xf32>
  %5 = "tfl.add"(%1, %4) {fused_activation_function = "NONE"} : (tensor<1x5x5x3xf32>, tensor<1x1x1x3xf32>) -> tensor<1x5x5x3xf32>

// -----

// CHECK-LABEL: testLiftDotConcatLHSAndRHS
func.func @testLiftDotConcatLHSAndRHS(%arg0: tensor<1x72x128xf32>, %arg1: tensor<1x128x72xf32>, %arg2: tensor<1x72x128xf32>, %arg3: tensor<1x128x72xf32>, %arg4: tensor<1x72x128xf32>, %arg5: tensor<1x128x72xf32>, %arg6: tensor<1x72x128xf32>, %arg7: tensor<1x128x72xf32>) -> tensor<4x72x72xf32> {
  %0 = "mhlo.dot_general"(%arg0, %arg1) {
    dot_dimension_numbers = #mhlo.dot<

  // attribute `_quantization_method` set to `"no_quantization {}"`.
  constexpr absl::string_view kXlaCallModuleOpWithQuantizationMethodAttr =
      R"mlir(
    func.func @main(%arg0: tensor<1x1x3xf32>, %arg1: tensor<3x4xf32>) -> tensor<1x1x4xf32> {

  %prelu = "tfl.prelu"(%arg0, %cst) : (tensor<1x10x10x3xf32>, tensor<1x1x3xf32>) -> tensor<1x10x10x3xf32>
  func.return %prelu : tensor<1x10x10x3xf32>

// CHECK: %[[cst:.*]] = arith.constant dense<[{{\[}}[1.66394591, 3.61694336, 2.0382936]]]> : tensor<1x1x3xf32>