/// 2. realValue is an elements attribute:
/// (realValue: DenseElementsAttr[tensor<2x2xf32>],
///  quantizedElementType: UniformQuantizedType[i8:f32])
///   -> (DenseElementsAttr[tensor<2x2xi8>], outConvertedType: tensor<2x2xi8>)
Attribute quantizeAttr(Attribute realValue,
                       quant::QuantizedType quantizedElementType,
                       Type &outConvertedType);

/// Converts an attribute from a type based on

}

func.func @RemoveTrivialSubInt8(%arg0: tensor<2x2xi8>) -> tensor<2x2xi8> {
  %cst = arith.constant dense<0> : tensor<2x2xi8>
  %0 = "tf.Sub"(%arg0, %cst) : (tensor<2x2xi8>, tensor<2x2xi8>) -> tensor<2x2xi8>
  func.return %0 : tensor<2x2xi8>

  // CHECK-LABEL: RemoveTrivialSubInt8
  // CHECK-NEXT: return %arg0 : tensor<2x2xi8>
}

func.func @RemoveTrivialMul(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {

  // CHECK: %[[CONVERT_3:.*]] = mhlo.bitcast_convert %[[REQUANTIZE]] : (tensor<2x2x!quant.uniform<i8:f32:0, {3.000000e+00:5,4.000000e+00:6}>>) -> tensor<2x2xi8>

    %22 = "tf.Identity"(%21) {device = ""} : (tensor<2x2xi8>) -> tensor<2x2xi8>
    %23 = "tf.Identity"(%22) {device = ""} : (tensor<2x2xi8>) -> tensor<2x2xi8>
    %24 = "tf.Cast"(%23) : (tensor<2x2xi8>) -> tensor<2x2xi32>
    %25 = "tf.Sub"(%24, %cst_4) : (tensor<2x2xi32>, tensor<i32>) -> tensor<2x2xi32>
    %26 = "tf.Cast"(%25) : (tensor<2x2xi32>) -> tensor<2x2xf32>

func.func @no_pushdown_gather_with_no_reduction(%arg0: tensor<2xi32>) -> tensor<2x2xf32> {
  %w = "tfl.pseudo_qconst"() {qtype = tensor<2x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<2x2xi8>} : () -> tensor<2x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
  %dq_w = "tfl.dequantize"(%w) : (tensor<2x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<2x2xf32>

  %weight_scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %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>

  func.return %1 : tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>

// CHECK:  %[[cst:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>, value = dense<-1> : tensor<2x2xi8>}>

  %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: return %[[output]] : tensor<6x3xf32>

// TF-DAG: %[[cc_0:.*]] = "tf.ConcatV2"(%[[pc_1]], %[[pc_5]], %[[cst_0]]) : (tensor<2x2xi8>, tensor<2x2xi8>, tensor<i32>) -> tensor<2x4xi8>
// TF-DAG: %[[pc_7:.*]] = "tf.PartitionedCall"(%[[cc_0]], %[[cst_6]], %[[cst_5]]) <{config = "", config_proto = "", executor_type = "", f = @dequantize_i8}
// TF-DAG: return %[[pc_7]]

    %0 = stablehlo.convert %arg0 : (tensor<1x2xf32>) -> tensor<1x2xi8>
    return %0 : tensor<1x2xi8>
  }
// CHECK: @uniform_quantize_0
  func.func private @uniform_quantize_1(%arg0: tensor<1x3xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1x1xi8>) -> tensor<1x3xi8> {
    %0 = stablehlo.convert %arg0 : (tensor<1x3xf32>) -> tensor<1x3xi8>
    return %0 : tensor<1x3xi8>
  }