code: def gen_slice_scatter_strategy(
      mesh: DeviceMesh, op_schema: OpSchema
  ) -> StrategyType:
      # 1. number of dimensions in input and src need to match.
      # 2. number of elements on all non-dim need to match between input and src.
      # 3. numer of elements in src in dim need to match the slice size.
      # Given the above:
      # - We suggest for src to follow the sharding of input, except on the scatter dimension,
      #   where our best bet for now is to make them replicated as a fall-back.
      #   TODO: Ideally we'd like to make sure the output is re-sharded afterwards to keep input sharding.
  
      input_strategy = op_schema.args_schema[0]
      assert isinstance(input_strategy, OpStrategy)
      input_ndim = input_strategy.output_ndim
      slice_dim = (
          cast(int, op_schema.args_schema[2])
          if len(op_schema.args_schema) > 2
          else 0
      )
      slice_dim = normalize_dim(slice_dim, input_ndim)
  
      slice_scatter_strategy = OpStrategy([])
      # by default follow the input strategy for both input and src
      for arg_strategy in input_strategy.strategies:
          arg_spec = arg_strategy.out_spec
          if not (
              is_tensor_dim_sharded(arg_spec, dim=slice_dim)
              or is_tensor_partial(arg_spec)
          ):
              # only add the strategy if the slice_scatter dim is not sharded or partial
              slice_scatter_strategy.strategies.append(
                  PlacementStrategy(output_spec=arg_spec)
              )
  
      if not slice_scatter_strategy.strategies:
          # if all strategies are filtered out, replicating all specs on slice_scatter dim
          # of the input strategy, and use that as the op strategy
          for arg_strategy in input_strategy.strategies:
              arg_spec = arg_strategy.out_spec
              replicate_spec = DTensorSpec(
                  mesh,
                  replicate_tensor_dim(
                      arg_spec.placements, dim=slice_dim
                  ),
              )
              slice_scatter_strategy.strategies.append(
                  PlacementStrategy(output_spec=replicate_spec)
              )
      return slice_scatter_strategy
  
imports: from typing import cast, List, Optional, Sequence, Tuple
  import torch
  from torch.distributed._tensor._utils import compute_local_shape
  from torch.distributed._tensor.op_schema import OpSchema, OpStrategy, OutputSharding, PlacementStrategy, RuntimeSchemaInfo, StrategyType
  from torch.distributed._tensor.ops.common_rules import pointwise_rule
  from torch.distributed._tensor.ops.utils import is_tensor_dim_sharded, is_tensor_partial, normalize_dim, prod, register_op_strategy, register_prop_rule
  from torch.distributed._tensor.placement_types import _Partial, DTensorSpec, Placement, Replicate, Shard
  from torch.distributed.device_mesh import DeviceMesh
label_desc: Use image node_id 9 for calling a global function with example usage: gen_slice_scatter_strategy(mesh, op_schema) and returns: slice_scatter_strategy