code: def __init__(self, name: str, k: float):
      self.name = name
      self.outputs_schema = [
          BatchTensorDescriptor.from_tensor(torch.randn(1, 2))
      ]
      self.grad_inputs_schema = [
          BatchTensorDescriptor.from_tensor(torch.randn(1, 2))
      ]
      self.forward_pool = DummyPool(k)
      self.backward_pool = DummyPool(k)
  
imports: from __future__ import annotations
  import asyncio
  import math
  from typing import Any, Dict
  import pytest
  import torch
  from hivemind.compression import deserialize_tensor_stream, deserialize_torch_tensor, serialize_torch_tensor
  from hivemind.dht import DHT
  from hivemind.moe.server.connection_handler import ConnectionHandler
  from hivemind.moe.server.module_backend import ModuleBackend
  from hivemind.moe.server.task_pool import TaskPool
  from hivemind.p2p.p2p_daemon_bindings.control import DEFAULT_MAX_MSG_SIZE, P2PHandlerError
  from hivemind.proto import runtime_pb2
  from hivemind.utils.asyncio import amap_in_executor, iter_as_aiter
  from hivemind.utils.serializer import MSGPackSerializer
  from hivemind.utils.streaming import split_for_streaming
  from hivemind.utils.tensor_descr import BatchTensorDescriptor
label_desc: Use image node_id 1 to create a new DummyModuleBackend object from inherited base classes: ModuleBackend with example: obj = DummyModuleBackend(name, k)