//
  // The value will override the previous value - that is, no "merging" of
  // existing and given constraints will be performed.
  virtual Status SetDeviceName(const char* name) = 0;

  virtual Status AddInput(AbstractTensorHandle* input) = 0;
  virtual Status AddInputList(
      absl::Span<AbstractTensorHandle* const> inputs) = 0;
  virtual Status Execute(absl::Span<AbstractTensorHandle*> retvals,

                         TF_AbstractTensor* const* inputs, TF_OutputList* o,
                         TF_Status* s) {
  for (int i = 0; i < num_inputs; i++) {
    tsl::Set_TF_Status_from_Status(s, unwrap(op)->AddInput(unwrap(inputs[i])));
    if (TF_GetCode(s) != TF_OK) {
      return;
    }
  }
  int num_outputs = unwrap(o)->expected_num_outputs;
  tsl::Set_TF_Status_from_Status(
      s, unwrap(op)->Execute(

  auto call_op = AbstractOperationPtr(ctx->CreateOperation());
  TF_RETURN_IF_ERROR(
      call_op->Reset(fname.c_str(), /*raw_device_name=*/nullptr));
  for (auto t : inputs) {
    TF_RETURN_IF_ERROR(call_op->AddInput(t));
  }
  int num_outputs = outputs.size();
  return call_op->Execute(outputs, &num_outputs);
}

Status VerifySupportedSignature(TaggedValue signature) {
  if (signature.type() == TaggedValue::Type::TENSOR_SPEC) {

			expectValue: types.Int(1),
		},
		{
			name:        "add_quantity",
			expr:        `quantity("50k").add(quantity("20")) == quantity("50.02k")`,
			expectValue: trueVal,
		},
		{
			name:        "add_int",
			expr:        `quantity("50k").add(20).isLessThan(quantity("50020"))`,
			expectValue: falseVal,
		},
		{
			name:        "sub_quantity",

  Status SetDeviceName(const char* name) override {
    // TODO(srbs): Implement this.
    device_name_ = name;
    return absl::OkStatus();
  }

  Status AddInput(AbstractTensorHandle* input) override {
    GraphTensor* t = dyn_cast<GraphTensor>(input);
    if (!t) {
      return tensorflow::errors::InvalidArgument(
          "Unable to cast input to GraphTensor");
    }

    // Rewrite pad_op before Convolutioin.
    if (failed(HandlePad(pad_op, filter_shape[0], block_size))) return;
    auto pad_input = pad_op.getInput();
    if (auto block_arg = mlir::dyn_cast<mlir::BlockArgument>(pad_input)) {
      // Change on device function type/shape.
      HandleFuncOp(block_arg.getOwner()->getParentOp());
    }
  }

  // Handle Conv2D input, stride and filter.

// CHECK: %[[q_input:.*]] = "quantfork.qcast"(%arg0) : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0011764706057660721:-43>>
// CHECK: %[[dq_input:.*]] = "quantfork.dcast"(%[[q_input]]) : (tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0011764706057660721:-43>>) -> tensor<1x3x4x3xf32>

    // [0, 1] is symmetric across the line Y=0.5.
    Value abs_input = rewriter.create<AbsOp>(loc, input);
    Value abs_input_floor = rewriter.create<FloorOp>(loc, abs_input);
    Value abs_frac_input =
        rewriter.create<SubOp>(loc, abs_input, abs_input_floor);

    // Convert values of abs_frac_input > 0.5 to (1 - frac_input) to improve

  func.return %5, %6, %7, %8, %9 : tensor<f32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>
}

// CHECK-LABEL: @add_int
func.func @add_int() -> (tensor<i32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) {
  %0 = arith.constant dense<8> : tensor<i32>
  %1 = arith.constant dense<1> : tensor<i32>

  %2 = arith.constant dense< 4> : tensor<4xi32>

    if (n->IsOp()) {
      NodeDef* node_def = n->mutable_def();
      node_def->clear_input();
      for (const Edge* e : n->in_edges()) {
        Node* src = e->src();
        if (src->IsOp()) {
          Graph::AddInput(node_def, src->name(), e->src_output());
        }
      }
    }
  }
}
absl::StatusOr<std::unique_ptr<Graph>> Exporter::Convert(
    const GraphExportConfig& configs, const Dialect* tf_dialect,