code: def _get_admissible_tensor_parallel_degrees(
      hf_model_config: dict,
  ) -> int:
      """Placeholder docstring"""
      available_gpus = _get_available_gpus()
  
      attention_heads = None
      for variant in ATTENTION_HEAD_NAME_VARIENTS:
          attention_heads = hf_model_config.get(variant)
          if attention_heads:
              break
  
      if not attention_heads:
          return 1
  
      admissible_parallel_degrees = []
      for i in (n + 1 for n in reversed(range(len(available_gpus)))):
          if attention_heads % i == 0:
              admissible_parallel_degrees.append(i)
  
      logger.info(
          "Model can be sharded across %s GPUs",
          admissible_parallel_degrees,
      )
  
      return admissible_parallel_degrees
  
file_path: sagemaker-python-sdk/src/sagemaker/serve/model_server/djl_serving/utils.py
imports: from __future__ import absolute_import
  from urllib.error import HTTPError
  import math
  import logging
  from sagemaker.serve.utils.types import _DjlEngine
  from sagemaker.djl_inference import defaults
  from sagemaker.djl_inference.model import _get_model_config_properties_from_hf
  from sagemaker.serve.utils.local_hardware import _get_available_gpus
  from sagemaker.serve.builder.schema_builder import SchemaBuilder
label_desc: Use image node_id 10 for calling a global function with example usage: _get_admissible_tensor_parallel_degrees(hf_model_config) and returns: admissible_parallel_degrees, int