code: def _allocate_devices(
      hostfile_path: str,
      tensor_parallel: int,
      replica_num: int,
      device_map: Dict[str, List[List[int]]] = DEVICE_MAP_DEFAULT,
  ):
      resource_pool = fetch_hostfile(hostfile_path)
      assert (
          resource_pool is not None and len(resource_pool) > 0
      ), f"No hosts found in {hostfile_path}"
  
      # If no device map was provided, we generate one based on the resources we find in the hostfile
      if device_map == DEVICE_MAP_DEFAULT:
          device_map = {}
          filled_slots = 0
          for host, slots in resource_pool.items():
              slots_to_fill = min(
                  slots // tensor_parallel, replica_num - filled_slots
              )
              filled_slots += slots_to_fill
              device_map[host] = [
                  list(
                      range(
                          i * tensor_parallel, (i + 1) * tensor_parallel
                      )
                  )
                  for i in range(slots_to_fill)
              ]
  
      # Assert that we have the correct number of mappings
      device_map_slots = sum(
          [len(slots_list) for slots_list in device_map.values()]
      )
      if device_map_slots < replica_num:
          raise ValueError(
              f"Only able to place {device_map_slots} replicas, but {replica_num} replicas were requested."
          )
      if device_map_slots > replica_num:
          raise ValueError(
              f"Device map contains {device_map_slots} mappings, but only {replica_num} replicas were requested. There must be a 1:1 mapping."
          )
  
      replica_pool = []
      # Fill the available slots with replicas
      for host, slots_list in device_map.items():
          if host not in resource_pool:
              raise ValueError(f"Host {host} not found in hostfile")
          for slots in slots_list:
              if len(slots) != tensor_parallel:
                  raise ValueError(
                      f"Number of devices must match tensor_parallel. Found {len(slots)} devices for host {host}, but tensor_parallel={tensor_parallel}"
                  )
              replica_pool.append((host, slots))
  
      return replica_pool
  
imports: import os
  import string
  from typing import List, Optional, Union, Dict, Any, Literal
  from deepspeed.runtime.config_utils import DeepSpeedConfigModel
  from deepspeed.launcher.runner import DLTS_HOSTFILE, fetch_hostfile
  from deepspeed.inference import RaggedInferenceEngineConfig
  from mii.constants import DeploymentType, TaskType, ModelProvider
  from mii.errors import DeploymentNotFoundError
  from mii.modeling.tokenizers import MIITokenizerWrapper
  from mii.pydantic_v1 import BaseModel, Field, root_validator, validator
  from mii.utils import generate_deployment_name, get_default_task, import_score_file
label_desc: Use image node_id 1 for calling a global function with example usage: _allocate_devices(hostfile_path, tensor_parallel, replica_num, device_map) and returns: replica_pool