code: def _compute_buckets_lp(
      params: List[ColoParameter], norm_type: float
  ) -> float:
      if len(params) == 0:
          return 0.0
      buckets: Dict[
          Optional[ProcessGroup], List[ColoParameter]
      ] = defaultdict(list)
      for p in params:
          if p.is_replicate():
              buckets[None].append(p)
          else:
              buckets[p.get_process_group().tp_process_group()].append(
                  p
              )
      total_lp = 0.0
      for group, bucket in buckets.items():
          local_lp = _compute_local_lp(bucket, norm_type)
          if group is not None:
              local_lp_tensor = torch.tensor(
                  [local_lp], device=torch.cuda.current_device()
              )
              if norm_type == inf:
                  dist.all_reduce(
                      local_lp_tensor, op=dist.ReduceOp.MAX, group=group
                  )
              else:
                  dist.all_reduce(local_lp_tensor, group=group)
              local_lp = local_lp_tensor.item()
          if norm_type == inf:
              total_lp = max(total_lp, local_lp)
          else:
              total_lp += local_lp
      return total_lp
  
imports: from collections import defaultdict
  from contextlib import contextmanager
  from typing import Dict, List, Optional, Union
  import torch
  import torch.distributed
  from torch import inf
  from torch.nn.parameter import Parameter
  from colossalai.legacy.constants import IS_TENSOR_PARALLEL, NUM_PARTITIONS, TENSOR_PARALLEL_ATTRIBUTES
  from colossalai.legacy.context.parallel_mode import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.legacy.global_variables import tensor_parallel_env
  from colossalai.legacy.tensor import ProcessGroup
  from colossalai.tensor import ColoParameter
  from colossalai.utils.multi_tensor_apply import multi_tensor_applier
label_desc: Use image node_id 15 for calling a global function with example usage: _compute_buckets_lp(params, norm_type) and returns: total_lp, int