code: def _update_sharding_spec_for_transposed_weight_for_linear(
      strategy: ShardingStrategy, weight_name: str
  ) -> ShardingStrategy:
      """
      This function is a helper function used by both module node handler and function node handler. This function will
      convert the sharding spec for the transposed weight to the correct partition spec.
  
      Args:
          strategy (ShardingStrategy): the strategy generated by the strategy generator.
          weight_name (str): the name of the OperationData object for the weight.
      """
      # switch the dimensions of the transposed weight
      sharding_spec = strategy.get_sharding_spec_by_name(weight_name)
      op_data = strategy.get_op_data_by_name(weight_name)
      assert (
          op_data.logical_shape[0] == op_data.data.shape[1]
          and op_data.logical_shape[1] == op_data.data.shape[0]
      ), "Expected the logical shape  of the linear operator's weight is equal to transposed physical shape"
      dim_size = len(op_data.logical_shape)
      transpose_partition_dim(sharding_spec, 0, dim_size - 1)
      return strategy
  
imports: from typing import Dict, List, Union
  import torch
  import torch.nn.functional
  from colossalai.auto_parallel.tensor_shard.utils import transpose_partition_dim, update_partition_dim
  from colossalai.logging import get_dist_logger
  from colossalai.tensor.sharding_spec import ShardingNotDivisibleError
  from ..sharding_strategy import OperationData, OperationDataType, ShardingStrategy
  from .node_handler import MetaInfoModuleHandler, MetaInfoNodeHandler
  from .registry import operator_registry
  from .strategy import LinearProjectionStrategyGenerator, StrategyGenerator
label_desc: Use image node_id 1 for calling a global function with example usage: _update_sharding_spec_for_transposed_weight_for_linear(strategy, weight_name) and returns: strategy