code: def cast_and_place(tensor, dtype):
      if dtype.is_cuda:
          if (
              bf.nccl_built()
              and bf.local_size() > torch.cuda.device_count()
          ):
              raise EnvironmentError(
                  "Cannot run number of processes in one machine more than GPU device count"
                  " in NCCL environment"
              )
          return tensor.cuda(
              bf.rank() % torch.cuda.device_count()
          ).type(dtype)
      return tensor.type(dtype)
  
imports: import itertools
  import os
  import numpy
  import pytest
  import torch
  import networkx
  import bluefog.torch
  from common import mpi_env_rank_and_size
label_desc: Use image node_id 1 for calling a global function with example usage: cast_and_place(tensor, dtype) and returns: tensor, tensor