const int input_idx = 0;
  EXPECT_THAT(subgraph->tensors[input_idx]->type, Eq(TensorType_INT8));
  EXPECT_THAT(subgraph->tensors[input_idx]->name, Eq("input"));
  EXPECT_THAT(subgraph->tensors[input_idx]->quantization->scale, SizeIs(1));
  EXPECT_THAT(subgraph->tensors[input_idx]->quantization->zero_point,
              SizeIs(1));
  EXPECT_THAT(subgraph->tensors[input_idx]->quantization->scale[0],

                       cluster_info.resource_inputs.size());
  int input_idx = 0;
  for (const Output& o : cluster_info.non_constant_inputs) {
    if (debugging_opts.check_input_numerics && DataTypeIsFloating(o.type())) {
      ops::CheckNumerics check_numerics_op(
          s.WithOpName("check_input_", input_idx), o,
          absl::StrCat("CheckNumerics failed for input ", input_idx, "(",

          compilation_result, i));
    } else if (type == DT_RESOURCE) {
      int input_index =
          compilation_result->outputs[i].input_index - missing_ctx_input_prefix;
      TF_RET_CHECK(input_index >= 0 && input_index < ctx->num_inputs())
          << "Invalid input for outputs " << i << ": " << input_index;
      ctx->set_output(i, ctx->input(input_index));
    } else {
      TF_ASSIGN_OR_RETURN(
          Tensor output_tensor,

      indices.getType().getRank() != 1 || port[1] >= indices.getNumElements()) {
    return {};
  }

  int64_t input_idx = indices.getValues<IntegerAttr>()[port[1]].getInt();
  if (input_idx >= params_ty.getDimSize(0)) return {};

  ValuePort req(params);
  req.port.push_back(input_idx);
  return req;
}

ValuePort ComputeInputComponentFor(Operation* op, ArrayRef<unsigned int> port) {

                                                   int32_t input_index,
                                                   int32_t output_index) {
  auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelContext*>(ctx);
  if (cc_ctx->input_dtype(input_index) != tensorflow::DT_RESOURCE) {
    cc_ctx->forward_ref_input_to_ref_output(input_index, output_index);
  }
}

  void runOnOperation() override;
};

constexpr char kAliasingAttr[] = "tf.aliasing_output";
constexpr int kUnassigned = -1;

struct AliasInfo {
  AliasInfo() : input_index(kUnassigned), output_index(kUnassigned) {}
  int input_index;
  int output_index;
};

// Idenitfy tf_device.cluster_func input-output alias pairs.
// This is currently conservative, primarily handling the following base case:
// ```

    TFE_OpReset(op_.get(), operation_name, device_.c_str(), status);
    if (TF_GetCode(status) != TF_OK) return;
  }
  TFE_OpAddAttrs(op_.get(), attributes);
  for (int input_index = 0; input_index < inputs.size(); ++input_index) {
    TFE_OpAddInput(op_.get(), inputs[input_index], status);
    if (TF_GetCode(status) != TF_OK) return;
  }
  std::vector<TFE_TensorHandle*> unwrapped_results(expected_max_outputs);

                         int input_index, bool use_function,
                         AbstractTensorHandle** numerical_grad) {
  vector<AbstractTensorHandle*> theta_inputs(inputs.size());
  for (int i{}; i < inputs.size(); ++i) {
    theta_inputs[i] = inputs[i];
  }

  AbstractTensorHandle* theta =
      theta_inputs[input_index];  // parameter we are grad checking

        %weight = "tf.Const"() {value = dense<1> : tensor<1024x24x24x3xi8>} : () -> tensor<1024x24x24x3xi8>
        %input_i32 = "tf.Cast"(%weight) : (tensor<1024x24x24x3xi8>) -> tensor<1024x24x24x3xi32>
        %output = "tf.Sub"(%input_i32, %zp) : (tensor<1024x24x24x3xi32>, tensor<i32>) -> tensor<1024x24x24x3xi32>
        %cast = "tf.Cast"(%output) : (tensor<1024x24x24x3xi32>) -> tensor<1024x24x24x3xf32>

                                const_op->getResult(0));
    auto dq = rewriter.create<DQ>(const_op->getLoc(), expressed_type, q);
    op.setOperand(input_index, dq.getResult());
    return success();
  }

  LogicalResult replaceStatsOp(
      SourceOp op, quantfork::StatisticsOp stats_op, int input_index,
      const operator_property::TensorProperty& tensor_property,
      PatternRewriter& rewriter) const {