class_docstr: A naive implementation which shard each tensor evenly over all ranks
code: def shard(
      self,
      tensor_list: List[ShardedTensor],
      process_group: Optional[dist.ProcessGroup] = None,
  ):
      for t in tensor_list:
          self._shard_tensor(t, process_group)
  
file_path: colossalai/colossalai/legacy/zero/shard_utils/tensor_shard_strategy.py
imports: from typing import List, Optional
  import torch
  import torch.distributed
  from colossalai.legacy.zero.gemini.tensor_utils import colo_model_data_tensor_move_inline
  from colossalai.legacy.zero.shard_utils import BaseShardStrategy
  from colossalai.legacy.zero.shard_utils.commons import get_shard
  from colossalai.legacy.zero.sharded_param.sharded_tensor import ShardedTensor
  from colossalai.utils import get_current_device
label_desc: Use image node_id 1 for calling the TensorShardStrategy obj's underlying member method code with example usage: obj.shard(tensor_list, process_group) without return types