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,

                                                   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);
  }
}

    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);

        %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 {

           << absl::StrJoin(xla_output_description.shape.dim_sizes(), ", ")
           << ')';
    if (xla_output_description.input_index >= 0)
      output << " input_index=" << xla_output_description.input_index;
    if (xla_output_description.is_constant) output << " constant";
    if (xla_output_description.is_tensor_list) output << " tensor_list";
    output << '\n';
  }

                           GradientFunction* gradient_function,
                           const string& op_name) {
  std::vector<int64_t> input_ids(inputs.size());
  std::vector<tensorflow::DataType> input_dtypes(inputs.size());
  for (int i = 0; i < inputs.size(); i++) {
    input_ids[i] = ToId(inputs[i]);
    input_dtypes[i] = inputs[i]->DataType();
  }
  std::vector<TapeTensor> tape_tensors;

const char* TF_GetNumberAttrForOpListInput(const char* op_name, int input_index,
                                           TF_Status* status) {
  const tensorflow::OpDef* op_def = nullptr;
  status->status =
      tensorflow::OpRegistry::Global()->LookUpOpDef(op_name, &op_def);
  if (!status->status.ok()) return nullptr;

  if (input_index >= op_def->input_arg_size() || input_index < 0) {
    status->status = tensorflow::errors::InvalidArgument(

    return false;
  }
  if (filter_index == input_indices[0]) {
    input_index = input_indices[1];
  } else if (filter_index == input_indices[1]) {
    input_index = input_indices[0];
  } else {
    return false;
  }

  const QuantState& input_state = GetOperandQuantState(op, input_index);
  const QuantState& filter_state = GetOperandQuantState(op, filter_index);