class_docstr: The NodeHandler is an abstract class used to generate every possible strategies for an operator node.
  
  Args:
      node (Node): the input node in node argument list.
      device_mesh (DeviceMesh): A logical view of a physical mesh.
      strategies_vector (StrategiesVector): all the strategies generated in this handler will be recorded into the strategies_vector.
class_docstr_tok: ["The","NodeHandler","is","an","abstract","class","used","to","generate","every","possible","strategies","for","an","operator","node",".","Args",":","node","(","Node",")",":","the","input","node","in","node","argument","list",".","device_mesh","(","DeviceMesh",")",":","A","logical","view","of","a","physical","mesh",".","strategies_vector","(","StrategiesVector",")",":","all","the","strategies","generated","in","this","handler","will","be","recorded","into","the","strategies_vector","."]
docstr: Register different sharding strategies for the current node.
docstr_tok: ["Register","different","sharding","strategies","for","the","current","node","."]
code: def register_strategy(
      self, compute_resharding_cost: bool = True
  ) -> StrategiesVector:
      """
      Register different sharding strategies for the current node.
      """
      strategy_generators = self.get_strategy_generator()
      for generator in strategy_generators:
          strategies = generator.generate()
  
          # postprocess a strategy
          # postprocess can produce one strategy or multiple strategies
          post_processed_strategies_map = map(
              self.post_process, strategies
          )
          post_processed_strategies = []
  
          for strategy in post_processed_strategies_map:
              if isinstance(strategy, (list, tuple)):
                  post_processed_strategies.extend(strategy)
              else:
                  post_processed_strategies.append(strategy)
  
          # compute the resharding costs based on the previous node
          # strategies if specified
          if compute_resharding_cost:
              updated_strategies = map(
                  self.update_resharding_cost, post_processed_strategies
              )
              post_processed_strategies = list(updated_strategies)
  
          self.strategies_vector.extend(post_processed_strategies)
  
      # validating the correctness of the sharding strategy
      for strategy in self.strategies_vector:
          for op_data, sharding_spec in strategy.sharding_specs.items():
              if op_data.data is not None and isinstance(
                  op_data.data, torch.Tensor
              ):
                  check_sharding_spec_validity(
                      sharding_spec, op_data.data
                  )
  
      remove_strategy_list = []
      for strategy in self.strategies_vector:
          shard_axis_list = []
          last_axis = len(self.device_mesh.shape) - 1
          for op_data, sharding_spec in strategy.sharding_specs.items():
              if op_data.data is not None and isinstance(
                  op_data.data, torch.Tensor
              ):
                  for (
                      dim,
                      shard_axes,
                  ) in sharding_spec.dim_partition_dict.items():
                      for shard_axis in shard_axes:
                          if shard_axis not in shard_axis_list:
                              shard_axis_list.append(shard_axis)
  
          shard_level = len(shard_axis_list)
          using_last_axis = (
              last_axis in shard_axis_list or -1 in shard_axis_list
          )
          if (
              self.shard_option == ShardOption.SHARD
              and shard_level == 0
          ):
              remove_strategy_list.append(strategy)
          if (
              self.shard_option == ShardOption.FULL_SHARD
              and shard_level <= 1
          ):
              remove_strategy_list.append(strategy)
          if self.shard_option == ShardOption.SHARD_LAST_AXIS:
              if shard_level != 1 or using_last_axis == False:
                  remove_strategy_list.append(strategy)
  
      for strategy in remove_strategy_list:
          self.strategies_vector.remove(strategy)
  
      return self.strategies_vector
  
imports: from abc import ABC, abstractmethod
  from typing import Dict, List, Tuple, Union
  import torch
  from torch.fx.node import Node
  from colossalai.auto_parallel.meta_profiler.shard_metainfo import ShardMetaInfo, meta_register
  from colossalai.auto_parallel.tensor_shard.options import ShardOption, SolverPerference
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, ShardingSpec, ShardingStrategy, StrategiesVector, TrainCycleItem
  from colossalai.auto_parallel.tensor_shard.utils import check_sharding_spec_validity
  from colossalai.device.device_mesh import DeviceMesh
  from colossalai.logging import get_dist_logger
  from colossalai.tensor.shape_consistency import ShapeConsistencyManager
  from .strategy import StrategyGenerator
label_desc: Use image node_id 4 for calling the NodeHandler obj's underlying member method code with example usage: obj.register_strategy(compute_resharding_cost) and returns: self