code: def is_sagemaker_model_parallel_available():
      # Get the sagemaker specific mp parameters from smp_options variable.
      smp_options = os.getenv("SM_HP_MP_PARAMETERS", "{}")
      try:
          # Parse it and check the field "partitions" is included, it is required for model parallel.
          smp_options = json.loads(smp_options)
          if "partitions" not in smp_options:
              return False
      except json.JSONDecodeError:
          return False
  
      # Get the sagemaker specific framework parameters from mpi_options variable.
      mpi_options = os.getenv("SM_FRAMEWORK_PARAMS", "{}")
      try:
          # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
          mpi_options = json.loads(mpi_options)
          if not mpi_options.get("sagemaker_mpi_enabled", False):
              return False
      except json.JSONDecodeError:
          return False
      # Lastly, check if the `smdistributed` module is present.
      return importlib.util.find_spec("smdistributed") is not None
  
file_path: transformers/src/transformers/sagemaker/training_args_sm.py
imports: import importlib.util
  import json
  import os
  import warnings
  from dataclasses import dataclass, field
  import torch
  from ..training_args import TrainingArguments
  from ..utils import cached_property, is_sagemaker_dp_enabled, logging
label_desc: Use image node_id 1 for calling a global function with example usage: is_sagemaker_model_parallel_available() and returns: unknown, False, False, False, False