%cst_1 = "tfl.pseudo_qconst"() {qtype = tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<1x20xi8>} : () -> tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>
    %cst_2 = "tfl.no_value"() {value = unit} : () -> none
    %cst_3 = "tfl.pseudo_qconst"() {qtype = tensor<20x20x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<20x20xi8>} : () -> tensor<20x20x!quant.uniform<i8:f32, 0.006:-34>>

TEST_F(QuantizationReportTest, InitializeWithModuleOp) {
  constexpr absl::string_view kQuantizedDotGeneral = R"mlir(
    func.func @main(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> {
      %0 = stablehlo.constant() {value = dense<127> : tensor<2x3xi8>} : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32:1, {1.000000e+0,2.000000e+0,3.000000e+0}>>
      %1 = stablehlo.uniform_quantize %arg0 : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<i8:f32, 4.000000e+0>>

}

// CHECK-LABEL: @transpose_no_fold
func.func @transpose_no_fold(%arg0 : tensor<2xi32>) -> tensor<2x2xi32> {
  %cst = arith.constant dense<[[0, 1], [2, 3]]> : tensor<2x2xi32>

  // CHECK: tfl.transpose
  %0 = "tfl.transpose"(%cst, %arg0) : (tensor<2x2xi32>, tensor<2xi32>) -> tensor<2x2xi32>
  func.return %0 : tensor<2x2xi32>
}


// CHECK-LABEL: @transpose_1d
// Basic 1D identity

  func.return %0 : tensor<4xi64>
}

func.func @string() -> tensor<2x2x!tf_type.string> {
  // CHECK-LABEL: @string
  // CHECK: value = dense<{{\[\["1", "12"\], \["123", "1234"\]\]}}> : tensor<2x2x!tf_type.string>
  %0 = "tfl.pseudo_const"() { value = dense<[["1", "12"], ["123", "1234"]]> : tensor<2x2x!tf_type.string> } : () -> tensor<2x2x!tf_type.string>
  func.return %0 : tensor<2x2x!tf_type.string>
}

  func.return %0 : tensor<4xi64>
}

func.func @string() -> tensor<2x2x!tf_type.string> {
  // CHECK-LABEL: @string
  // CHECK: value = dense<{{\[\["1", "12"\], \["123", "1234"\]\]}}> : tensor<2x2x!tf_type.string>
  %0 = "tfl.pseudo_const"() { value = dense<[["1", "12"], ["123", "1234"]]> : tensor<2x2x!tf_type.string> } : () -> tensor<2x2x!tf_type.string>
  func.return %0 : tensor<2x2x!tf_type.string>
}

  return %2 : tensor<?x2x2x1xf32>
}

// -----

// CHECK-LABEL: func @dot_general_add_add
func.func @dot_general_add_add(
    %lhs: tensor<?x?x6xi8>, %rhs: tensor<6x8xi8>,
    %zp_offset: tensor<8xi32>, %bias: tensor<8xi32>
  ) -> tensor<?x?x8xi32> {
  // CHECK: %[[dot:.*]] = "mhlo.dot_general"
  // CHECK: %[[combined:.*]] = chlo.broadcast_add %[[zp_offset:.*]], %[[bias:.*]]

                                             output_spatial_dimensions = #vhlo.tensor_v1<dense<[1, 2]> : tensor<2xi64>>,
                                             padding = #vhlo.tensor_v1<dense<0> : tensor<2x2xi64>>,
                                             precision_config = #vhlo.array_v1<[#vhlo<precision_v1 DEFAULT>, #vhlo<precision_v1 DEFAULT>]>,

  // CHECK-LABEL: func.func private @merge_relu_fusion
  func.func private @merge_relu_fusion(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> {
    %0 = stablehlo.constant() {value = dense<127> : tensor<4x3xi8>} : () -> tensor<4x3x!quant.uniform<i8<-127:127>:f32:1, {5.000000e-03,5.000000e-03,5.000000e-03}>>
    %1 = stablehlo.uniform_quantize %arg0 : (tensor<1x4xf32>) -> tensor<1x4x!quant.uniform<i8:f32, 6.000000e-03:-128>>