class_docstr: Deal with case 1 and 2.
class_docstr_tok: ["Deal","with","case","1","and","2","."]
code: def collate_strategies(self) -> List[ShardingStrategy]:
      strategy_list = []
      getitem_index = self.op_data["index"].data
      for index, strategy in enumerate(
          self.predecessor_node.strategies_vector
      ):
          try:
              logger = get_dist_logger()
              dim_partition_dict_mapping = {}
              communication_action_mapping = {}
              dim_partition_dict_for_input = copy.deepcopy(
                  strategy.output_sharding_specs[
                      self.op_data["input"]
                  ].dim_partition_dict
              )
  
              int_index = False
              if isinstance(getitem_index, int):
                  int_index = True
                  getitem_dims = [
                      0,
                  ]
                  shift_length = 1
              elif isinstance(getitem_index, slice):
                  getitem_dims = [
                      0,
                  ]
              else:
                  getitem_dims = [i for i in range(len(getitem_index))]
                  if isinstance(getitem_index[0], int):
                      int_index = True
                      shift_length = len(getitem_index)
  
              gather_dims = []
              for dim in getitem_dims:
                  if dim in dim_partition_dict_for_input:
                      gather_dims.append(dim)
  
              for dim in gather_dims:
                  dim_partition_dict_for_input.pop(dim)
              dim_partition_dict_for_output = copy.deepcopy(
                  dim_partition_dict_for_input
              )
  
              if int_index:
                  shift_dim_partition_dict_for_output = {}
                  for (
                      dim,
                      mesh_dim_list,
                  ) in dim_partition_dict_for_output.items():
                      shift_dim_partition_dict_for_output[
                          dim - shift_length
                      ] = mesh_dim_list
                  dim_partition_dict_for_output = (
                      shift_dim_partition_dict_for_output
                  )
  
              dim_partition_dict_mapping = {
                  "input": dim_partition_dict_for_input,
                  "output": dim_partition_dict_for_output,
              }
              sharding_spec_mapping = self.to_sharding_spec_mapping(
                  dim_partition_dict_mapping
              )
  
              name = f'{sharding_spec_mapping["output"].sharding_sequence} = {sharding_spec_mapping["input"].sharding_sequence}_{index}'
  
              strategy = self.get_sharding_strategy(
                  name=name,
                  sharding_spec_mapping=sharding_spec_mapping,
                  communication_action_mapping=communication_action_mapping,
              )
          except ShardingSpecException as e:
              logger.debug(e)
              continue
          strategy_list.append(strategy)
  
      for strategy in strategy_list:
          self.update_communication_cost(strategy)
          self.update_compute_cost(strategy)
          self.update_memory_cost(strategy)
  
      return strategy_list
  
file_path: colossalai/colossalai/auto_parallel/tensor_shard/node_handler/strategy/getitem_generator.py
imports: import copy
  from typing import List
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, ShardingStrategy, TrainCycleItem
  from colossalai.logging import get_dist_logger
  from colossalai.tensor.sharding_spec import ShardingSpecException
  from .strategy_generator import FollowingStrategyGenerator
label_desc: Use image node_id 1 for calling the TensorStrategyGenerator obj's underlying member method code with example usage: obj.collate_strategies() and returns: strategy_list