class_docstr: Data Parallelism mode.
class_docstr_tok: ["Data","Parallelism","mode","."]
docstr: optimize a distributed graph with a set of optimization passes
docstr_tok: ["optimize","a","distributed","graph","with","a","set","of","optimization","passes"]
code: def transform_and_compile(self, gm: GraphModule) -> GraphModule:
      """optimize a distributed graph with a set of optimization passes"""
      # TODO: add more necessary arguments to this interface.
      return self._gm_passes(gm)
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import torch
  import torch.distributed
  import torch.utils._pytree
  from torch._subclasses import FakeTensorMode
  from torch.distributed._spmd.data_parallel import DataParallelStyle, partition_data_parallel
  from torch.distributed._spmd.distribute import _convert_to_distributed, Schema
  from torch.distributed._tensor import DeviceMesh, Placement, Replicate, Shard
  from torch.fx import GraphModule
label_desc: Use image node_id 3 for calling the DataParallel obj's underlying member method code with example usage: obj.transform_and_compile(gm) and returns: self