return false;
      }
    }

    if (input_matchers) {
      if (input_matchers->size() != node->num_inputs()) {
        if (listener->IsInterested()) {
          *listener << "\nexpected " << input_matchers->size()
                    << " inputs but node has " << node->num_inputs();
        }
        return false;
      }

      for (int input_idx = 0, e = input_matchers->size(); input_idx < e;

  }
  return absl::OkStatus();
}

std::vector<int> GetResourceVariableIndicesFromContext(OpKernelContext* ctx) {
  std::vector<int> out;
  for (int64 i = 0; i < ctx->num_inputs(); i++) {
    if (ctx->input(i).dtype() == DT_RESOURCE) {
      out.push_back(i);
    }
  }
  return out;
}

Status CreateVariableInfoLookup(
    absl::Span<VariableInfo const> variable_args,

  if (num_constant_inputs < 0 || num_resource_inputs < 0 ||
      num_constant_inputs + num_resource_inputs > n->num_inputs()) {
    return errors::InvalidArgument(
        "Invalid number of constant/resource arguments to XLA kernel.");
  }

  int num_non_constant_inputs =
      n->num_inputs() - num_constant_inputs - num_resource_inputs;

  std::vector<const Edge*> input_edges_vector;

    auto input = pos_and_input.value();
    bool is_packed = input.getIsPacked();
    const int num_operands = input->getNumOperands();
    int num_inputs = is_packed ? 1 : num_replicas;
    if (num_operands != num_inputs)
      return input->emitOpError() << "requires " << num_inputs << " operands";
    if (is_packed) {
      packed_inputs.push_back(input->getOperand(0));
      packed_ops.push_back(input);
    } else {

// if they are all fully defined; std::nullopt otherwise.
std::optional<std::vector<PartialTensorShape>> GetInferredInputShapes(
    int num_inputs, Node* send_from_host_node) {
  std::vector<PartialTensorShape> results(num_inputs);
  for (int i = 0; i < num_inputs; i++) {
    const Edge* e;
    if (!send_from_host_node->input_edge(i, &e).ok()) {
      return std::nullopt;
    }

                                 switch_input == n;
        if (is_loop_invariant) {
          shape_inference::InferenceContext* context =
              shape_refiner->GetContext(n);
          for (int i = 0; i < n->num_inputs(); i++) {
            const Node* input_node;
            if (n->input_node(i, &input_node).ok()) {
              auto shapes_and_types = context->input_handle_shapes_and_types(i);
              if (shapes_and_types) {

}

void TF_AddInputList(TF_OperationDescription* desc, const TF_Output* inputs,
                     int num_inputs) {
  std::vector<NodeBuilder::NodeOut> input_list;
  input_list.reserve(num_inputs);
  for (int i = 0; i < num_inputs; ++i) {
    input_list.emplace_back(&inputs[i].oper->node, inputs[i].index);
  }
  desc->node_builder.Input(input_list);
}

    FunctionType func_type = func.getFunctionType();
    int num_inputs = func_type.getNumInputs();
    int num_results = func_type.getNumResults();

    // For each argument type in function's arguments, change it to uranked
    // tensor type if it's a variant type.
    SmallVector<Type, 8> updated_argument_types;
    updated_argument_types.reserve(num_inputs);
    UpdateTensorListTypes<mlir::OperandRange>(

}

void TFE_OpAddInputList(TFE_Op* op, TFE_TensorHandle** inputs, int num_inputs,
                        TF_Status* status) {
  status->status = tensorflow::unwrap(op)->AddInputList(
      {reinterpret_cast<tensorflow::AbstractTensorHandle**>(
           tensorflow::unwrap(inputs)),
       static_cast<size_t>(num_inputs)});
}

extern int TFE_OpGetFlatInputCount(const TFE_Op* op, TF_Status* status) {

    ExecutableType** out_executable) {
  DCHECK_NE(out_executable, nullptr);
  VLOG(2) << "DeviceCompiler::Compile " << DebugString();

  if (VLOG_IS_ON(2)) {
    VLOG(2) << "num_inputs=" << args.size();
    for (int i = 0, end = args.size(); i < end; i++) {
      VLOG(3) << i << ": " << args[i].HumanString();
    }
  }
  TF_ASSIGN_OR_RETURN(auto signature,