code: def gen_slice_strategy(
      mesh: DeviceMesh, op_schema: OpSchema
  ) -> StrategyType:
      """Forward all shardings except the slice dimension."""
      defaults = (None, 0, None, None, 1)
      input_strategy, dim, start, end, step = (
          op_schema.args_schema + defaults[len(op_schema.args_schema) :]
      )
      assert isinstance(input_strategy, OpStrategy)
      input_shape = input_strategy.output_shape
      input_ndim = input_strategy.output_ndim
      assert isinstance(dim, int)
      if start is None:
          start = 0
      if end is None or end > input_shape[dim]:
          end = input_shape[dim]
      assert isinstance(start, int)
      assert isinstance(end, int)
      assert isinstance(step, int)
  
      # normalize args
      slice_dim = normalize_dim(dim, input_ndim)
      start = normalize_dim(start, input_shape[dim])
      end = normalize_dim(end, input_shape[dim])
  
      redundant_slice = (
          start == 0 and end == input_shape[dim] and step == 1
      )
  
      slice_strategy = OpStrategy([])
  
      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 redundant_slice
          ):
              # only add the strategy if the slice dim is not sharded
              out_spec = DTensorSpec(mesh, arg_spec.placements)
              slice_strategy.strategies.append(
                  PlacementStrategy(output_spec=out_spec)
              )
      if not slice_strategy.strategies:
          # if all strategies are filtered out, unsharding all specs on slice 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
              unshard_spec = DTensorSpec(
                  mesh,
                  unshard_tensor_dim(
                      arg_spec.placements, dim=slice_dim
                  ),
              )
              slice_strategy.strategies.append(
                  PlacementStrategy(output_spec=unshard_spec)
              )
      return slice_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 6 for calling a global function with example usage: gen_slice_strategy(mesh, op_schema) and returns: slice_strategy