// CHECK-NO-UNPACK: %[[QUANTIZE_1:.+]] = stablehlo.uniform_quantize %[[DOT]] : (tensor<1x3x!quant.uniform<i32:f32:1, {{.*}}>>) -> tensor<1x3x!quant.uniform<i8:f32, {{.*}}>>
// CHECK-NO-UNPACK: %[[DEQUANTIZE:.+]] = stablehlo.uniform_dequantize %[[QUANTIZE_1]] : (tensor<1x3x!quant.uniform<i8:f32, {{.*}}>>) -> tensor<1x3xf32>
// CHECK-NO-UNPACK: return %[[DEQUANTIZE]] : tensor<1x3xf32>

// -----

  %weight_zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

  // CHECK: "tf.AddV2"
  // CHECK: mhlo.constant
  // CHECK-SAME{LITERAL}: dense<[[1, 2], [3, 4]]> : tensor<2x2xi8>
  %0 = "tf.AddV2"(%input, %input) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>

  // CHECK: "mhlo.dot"
  // CHECK-SAME: (tensor<?x?xf32>, tensor<2x2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<?x?xf32>

  // CHECK-DAG: %[[LOSS_INP:.*]] = "tf.Mul"(%[[LOG_SOFTMAX]], %[[NEG_LABELS]]) : (tensor<2x3xf32>, tensor<2x3xf32>) -> tensor<2x3xf32>
  // CHECK-DAG: %[[SAFE_LOSS_INP:.*]] = "tf.SelectV2"(%[[IS_LABEL_ZERO]], %[[ZERO]], %[[LOSS_INP]]) : (tensor<2x3xi1>, tensor<f32>, tensor<2x3xf32>) -> tensor<2x3xf32>

// CHECK-NEXT: %[[qcat_2_0_3:.*]] = "tfl.quantize"(%[[cat_2_0_3]]) <{qtype = tensor<1x3x!quant.uniform<u8:f32, 0.0034775265291625379:140>>}> {volatile} : (tensor<1x3xf32>) -> tensor<1x3x!quant.uniform<u8:f32, 0.0034775265291625379:140>>
// CHECK-NEXT: %[[dqcat_2_0_3:.*]] = "tfl.dequantize"(%[[qcat_2_0_3]]) : (tensor<1x3x!quant.uniform<u8:f32, 0.0034775265291625379:140>>) -> tensor<1x3xf32>

  func.return %0 : tensor<1x2x!quant.uniform<i8:f32, 0.0078431372549019607:-128>>

// LEGACY:  "tfl.pseudo_qconst"() <{qtype = tensor<1x2x!quant.uniform<i8:f32, 0.0078431372549019607:-128>>, value = dense<{{\[\[}}-1, 127]]> : tensor<1x2xi8>}>
}

func.func private @testIfThen(tensor<*xf32>) -> tensor<*xf32>
func.func private @testIfElse(tensor<*xf32>) -> tensor<*xf32>

// CHECK-LABEL: NotQuantizeIf
func.func @NotQuantizeIf(%arg0: tensor<i1>,

  func.return %0: tensor<2xi32>
}

// -----

func.func @invalidSelect(%arg0: tensor<1x8xi1>, %arg1: tensor<1x8x8xi32>, %arg2: tensor<1x8x8xi32>) -> tensor<1x8x8xi32> {
  // expected-error @+1 {{requires that pred is a scalar OR has the same rank as t and e OR is a vector}}
  %0 = "tf.Select"(%arg0, %arg1, %arg2) : (tensor<1x8xi1>, tensor<1x8x8xi32>, tensor<1x8x8xi32>) -> tensor<1x8x8xi32>
  func.return %0: tensor<1x8x8xi32>
}

func.func @testConcatInvalidOperandDimSize(%arg0: tensor<1x2xi32>, %arg1: tensor<1x3xi32>) -> tensor<2x2xi32> {
  // expected-error @+1 {{'tfl.concatenation' op dimension size of dimension #1 of operand #1 must be equal to dimension size of dimension #1 of output, expected 2, got 3}}
  %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<1x2xi32>, tensor<1x3xi32>) -> tensor<2x2xi32>
  func.return %0 : tensor<2x2xi32>
}