code: def __init__(self, *args, **kwargs) -> None:
      super().__init__(*args, **kwargs)
  
      # set attributes to access module parameters for convenience
      assert (
          self.node.graph.owning_module is not None
      ), f"The graph is not associated with a module, please make sure it can be used to instantiate a GraphModule object."
      module = self.node.graph.owning_module.get_submodule(
          self.node.target
      )
      named_parameters = list(module.named_parameters(recurse=False))
      named_buffers = list(module.named_buffers(recurse=False))
      # convert named parameters from list to dict
      named_parameters = {k: v for k, v in named_parameters}
      named_buffers = {k: v for k, v in named_buffers}
      self.module = module
      self.named_parameters = named_parameters
      self.named_buffers = named_buffers
  
imports: from abc import ABC, abstractmethod
  from typing import Dict, List, Tuple, Union
  import torch
  from torch.fx.node import Node
  from colossalai.auto_parallel.meta_profiler.shard_metainfo import ShardMetaInfo, meta_register
  from colossalai.auto_parallel.tensor_shard.options import ShardOption, SolverPerference
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, ShardingSpec, ShardingStrategy, StrategiesVector, TrainCycleItem
  from colossalai.auto_parallel.tensor_shard.utils import check_sharding_spec_validity
  from colossalai.device.device_mesh import DeviceMesh
  from colossalai.logging import get_dist_logger
  from colossalai.tensor.shape_consistency import ShapeConsistencyManager
  from .strategy import StrategyGenerator
label_desc: Use image node_id 1 to create a new ModuleHandler object from inherited base classes: NodeHandler with example: obj = ModuleHandler()