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>{

    // TODO(ycao): Support constant output.
    out_desc.is_constant = false;
    TF_RETURN_IF_ERROR(XLAShapeToTensorShape(shape, &out_desc.shape));
    // Input_index is only meaningful for resource output. Setting it to
    // meaningless value -1 for non resource outputs.
    out_desc.input_index =
        it != output_to_input_alias.end() ? it->getSecond() : -1;
    // MLIR-based TF-Compiler bridge doesn't support tensorlist output yet.

  Input input = Shape(scope, op.input(0));
  Input input_min = op.input(1);
  Input input_max = op.input(2);
  int64_t axis;
  TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "axis", &axis));
  auto qdq_v4_grad = QuantizeAndDequantizeV4Grad(
      scope, grad_inputs[0], input, input_min, input_max,
      QuantizeAndDequantizeV4Grad::Axis(axis));
  grad_outputs->push_back(qdq_v4_grad.input_backprop);

    // output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])
    auto get_output_dim_for_same_padding = [](int64_t input_dim,
                                              int64_t stride_dim) -> int64_t {
      return std::ceil(input_dim / static_cast<double>(stride_dim));
    };
    return output_height ==
               get_output_dim_for_same_padding(input_height, stride_height) &&

  int num_inputs = TFE_OpGetFlatInputCount(other, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  for (int input_index = 0; input_index < num_inputs; ++input_index) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(other, input_index, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_OpAddInput(ret, input, status);

  // Checks preconditions to adjust bias scale.
  bool ShouldCheckBiasScale(Operation* op, int bias_index,
                            ArrayRef<int> input_indices,
                            QuantizedType quantized_type, int& input_index,
                            int& filter_index);

  // Preprocesses the constants by doing the following:
  //   - Duplicates constants if it is used by multiple ops. For example, if a

                                 llvm::StringRef crop_b_name) -> LogicalResult {
      int64_t input_dim = input_shape[spatial_dim_index];
      int64_t output_dim = output_shape[spatial_dim_index];
      if (!static_dims(input_dim, output_dim)) return success();

      int64_t input_dim_pad = input_dim * block_size;
      // If crops are unknown, the maximum output spatial dim size is input

  std::set<int> variables_updated;
  for (const auto& resource_update : compilation_result.resource_updates) {
    if (resource_update.modified) {
      variables_updated.insert(resource_update.input_index);
    }
  }
  return GetVariableInfosFromInputs(ctx->resource_manager(), ctx->device(),
                                    inputs, variable_indices,

                 << "multiples[" << i << "] = " << m;
        }

        if (!ShapedType::isDynamic(input_dim) &&
            !ShapedType::isDynamic(output_dim) && output_dim != input_dim * m) {
          return op.emitOpError()
                 << "requires input.shape[" << i << "] (" << input_dim << ")"
                 << " * " << m << " to be equal to "

    arg_nodes->resize(inputs.size());
    ret_nodes->resize(outputs.size());

    for (Node* n : GetOrderedNodes()) {
      // Handle inputs/arguments.
      auto input_it = inputs.find(n->name());
      if (input_it != inputs.end()) {
        (*arg_nodes)[input_it->second] = {n, 0};
      }

      // Handle outputs/returns.
      if (output_node_names.contains(n->name())) {