code: def parse_sm_config() -> Dict[str, Any]:
      """Parses SageMaker configuration.
  
      Returns:
          A dictionary of SageMaker config keys/values
          or an empty dict if not found.
          SM_TRAINING_ENV is a json string of the
          training environment variables set by SageMaker
          and is only available when running in SageMaker,
          but not in local mode.
          SM_TRAINING_ENV is set by the SageMaker container and
          contains arguments such as hyperparameters
          and arguments passed to the training job.
      """
      conf = {}
  
      if os.path.exists(sm_files.SM_PARAM_CONFIG) and os.path.exists(
          sm_files.SM_RESOURCE_CONFIG
      ):
          conf["sagemaker_training_job_name"] = os.getenv(
              "TRAINING_JOB_NAME"
          )
  
          # Hyperparameter searches quote configs...
          with open(sm_files.SM_PARAM_CONFIG) as fid:
              for key, val in json.load(fid).items():
                  cast = val.strip('"')
                  if re.match(r"^-?[\d]+$", cast):
                      cast = int(cast)
                  elif re.match(r"^-?[.\d]+$", cast):
                      cast = float(cast)
                  conf[key] = cast
  
      if "SM_TRAINING_ENV" in os.environ:
          try:
              conf = {
                  **conf,
                  **json.loads(os.environ["SM_TRAINING_ENV"]),
              }
          except json.JSONDecodeError:
              warnings.warn(
                  "Failed to parse SM_TRAINING_ENV not valid JSON string",
                  stacklevel=2,
              )
  
      return conf
  
imports: import json
  import os
  import re
  import warnings
  from typing import Any, Dict
  from .None import files
label_desc: Use image node_id 1 for calling a global function with example usage: parse_sm_config() and returns: conf