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],

          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);
  }
}

                                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';
  }

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);

  // the input.
  for (const auto& sharding_attr_and_index : llvm::enumerate(sharding_attrs)) {
    const auto& sharding_attr = sharding_attr_and_index.value();
    const auto input_index = sharding_attr_and_index.index();
    const auto& input_value = cluster_func_inputs[input_index];

    xla::OpSharding sharding;
    if (DecodeShardingAttribute(
            mlir::cast<mlir::StringAttr>(sharding_attr).getValue().str(),
            sharding)

  if (auto* opts = options.AsCallOnceOptions()) {
    uint32_t init_idx = opts->init_subgraph_index;
    if (init_idx >= func_names.size()) {
      return errors::InvalidArgument("subgraph with index not found: ",
                                     init_idx);
    }
    auto init_attr = builder.getStringAttr(func_names.at(init_idx));

    return llvm::SmallVector<mlir::NamedAttribute, 4>{