// CHECK-SAME: (tensor<3x4x5x!quant.uniform<i8:f32, 5.000000e-03>>, tensor<3x5x2x!quant.uniform<i8<-127:127>:f32, 6.000000e-03>>) -> tensor<3x4x2x!quant.uniform<i32:f32, 3.000000e-05>>
    // CHECK: %[[Q3:.*]] = stablehlo.uniform_quantize %0 : (tensor<3x4x2x!quant.uniform<i32:f32, 3.000000e-05>>) -> tensor<3x4x2x!quant.uniform<i8:f32, 0.13170163023705575:-1>>
    // CHECK: return %[[Q3]]

  // CHECK-NEXT: %8 = "mhlo.set_dimension_size"(%arg0, %7) <{dimension = 0 : i64}> : (tensor<3x4xi32>, tensor<i32>) -> tensor<3x4xi32>
  // CHECK-NEXT: %9 = mhlo.constant dense<4> : tensor<i32>
  // CHECK-NEXT: %10 = "mhlo.set_dimension_size"(%8, %9) <{dimension = 1 : i64}> : (tensor<3x4xi32>, tensor<i32>) -> tensor<3x4xi32>
  // CHECK-NEXT: %11 = mhlo.constant dense<0> : tensor<i32>

module attributes {tf.versions = {producer = 888 : i32}} {
func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> {
  %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32>
  // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32>
  %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}
}

// -----

func.func @equal_broadcast(%arg0: tensor<1xi32>, %arg1: tensor<1x2xi32>) -> tensor<1x2xi1> {
  // CHECK-DAG: %[[LHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<1> : tensor<1xi64>}>
  // CHECK-NEXT: mhlo.compare EQ, %[[LHS_BCAST]], %arg1
  %0 = "tf.Equal"(%arg0, %arg1) : (tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x2xi1>
  func.return %0: tensor<1x2xi1>
}

// CHECK-LABEL: func @equal_broadcast_no_incompatible_shapes_error

  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,

// Ensure constants roundtrip exactly

func.func @bool() -> tensor<4xi1> {
  // CHECK-LABEL: @bool
  // CHECK: value = dense<[false, true, true, false]> : tensor<4xi1>
  %0 = "tfl.pseudo_const"() { value = dense<[false, true, true, false]> : tensor<4xi1> } : () -> tensor<4xi1>
  func.return %0 : tensor<4xi1>
}

func.func @complex64() -> tensor<4xcomplex<f32>> {
  // CHECK-LABEL: @complex64

// Ensure constants roundtrip exactly

func.func @bool() -> tensor<4xi1> {
  // CHECK-LABEL: @bool
  // CHECK: value = dense<[false, true, true, false]> : tensor<4xi1>
  %0 = "tfl.pseudo_const"() { value = dense<[false, true, true, false]> : tensor<4xi1> } : () -> tensor<4xi1>
  func.return %0 : tensor<4xi1>
}

func.func @complex64() -> tensor<4xcomplex<f32>> {
  // CHECK-LABEL: @complex64

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

  // CHECK: %[[input:.*]] = "tf.ConcatV2"(%arg0, %arg1, %[[VAL:.*]]) : (tensor<3x3xi8>, tensor<3x3xi8>, tensor<i64>) -> tensor<6x3xi8>
  // CHECK: %[[input_qint:.*]] = "tf.Cast"(%[[input]]) <{Truncate = false}> : (tensor<6x3xi8>) -> tensor<6x3x!tf_type.qint8>
  // CHECK: %[[output:.*]] = "tf.UniformDequantize"(%[[input_qint]]

// CHECK:  %[[cst:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<2x4x!quant.uniform<i4:f32, 2.500000e-01:-1>>, value = dense<{{\[\[}}-4, -3, -2, -1{{\]}}, [0, 1, 2, 3{{\]\]}}> : tensor<2x4xi4>}>
// CHECK:  return %[[cst]]
}

// CHECK-LABEL: QuantizeDenseFloatConst
func.func @QuantizeDenseFloatConst() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {