code: def _create_placement_strategy(
      node: Node,
      mesh: DeviceMesh,
      placements: Tuple[Placement, ...],
      input_specs: Optional[Sequence[DTensorSpec]] = None,
  ) -> PlacementStrategy:
      """
      Util function to construct a placement strategy for a given node.
      """
      placement = PlacementStrategy(
          input_specs=input_specs,
          output_spec=DTensorSpec(
              mesh=mesh,
              placements=placements,
          ),
      )
      _populate_tensor_meta(node, placement.output_spec)
      return placement
  
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 7 for calling a global function with example usage: _create_placement_strategy(node, mesh, placements, input_specs) and returns: placement