class_docstr: This pass is responsible for transforming a single-device graph into a tensor parallel
  graph. It will mark the placement strategy of each node in the graph,
  partition the graph into distributed graph, then shard the parameters/buffers accordingly.
code: def __init__(
      self,
      rank: int,
      world_size: int,
      device_type: str,
      state_dict: Dict[str, torch.Tensor],
      graph_signature: ExportGraphSignature,
      parallel_strategies: Dict[str, ParallelStyle],
  ) -> None:
      super().__init__()
      self.rank = rank
      self.mesh = DeviceMesh(device_type, torch.arange(world_size))
      self.state_dict: Dict[str, torch.Tensor] = state_dict
      self.graph_signature = graph_signature
      self.parallel_strategies = parallel_strategies
  
imports: import copy
  import operator
  from typing import Any, cast, Dict, List, Optional, Sequence, Tuple
  import torch
  from torch._subclasses.fake_tensor import FakeTensor
  from torch.distributed._tensor import DeviceMesh, distribute_tensor, DTensor
  from torch.distributed._tensor.op_schema import DTensorSpec, OpSchema, OutputSharding, OutputSpecType, PlacementStrategy
  from torch.distributed._tensor.placement_types import Placement, Replicate, Shard, TensorMeta
  from torch.distributed._tensor.redistribute import redistribute_local_tensor
  from torch.distributed.tensor.parallel.style import ColwiseParallel, ParallelStyle
  from torch.export import ExportedProgram
  from torch.export.exported_program import ExportGraphSignature
  from torch.fx import GraphModule
  from torch.fx.experimental.proxy_tensor import make_fx
  from torch.fx.node import Node
  from torch.fx.passes.infra.pass_base import PassBase, PassResult
  from torch.fx.passes.shape_prop import _extract_tensor_metadata
  from torch.utils import _pytree
label_desc: Use image node_id 1 to create a new TensorParallelTransformPass object from inherited base classes: PassBase with example: obj = TensorParallelTransformPass(rank, world_size, device_type, state_dict, graph_signature, parallel_strategies)