class_docstr: An PyTorch SageMaker ``Model`` that can be deployed to a SageMaker ``Endpoint``.
class_docstr_tok: ["An","PyTorch","SageMaker","``","Model","``","that","can","be","deployed","to","a","SageMaker","``","Endpoint","``","."]
docstr: Determines if the framework corresponds to an and using MMS.
  
  Whether the framework version corresponds to an inference image using
  the Multi-Model Server (https://github.com/awslabs/multi-model-server).
  
  Returns:
      bool: If the framework version corresponds to an image using MMS.
docstr_tok: ["Determines","if","the","framework","corresponds","to","an","and","using","MMS",".","Whether","the","framework","version","corresponds","to","an","inference","image","using","the","Multi-Model","Server","(","https",":","\/\/github.com\/awslabs\/multi-model-server",")",".","Returns",":","bool",":","If","the","framework","version","corresponds","to","an","image","using","MMS","."]
code: def _is_mms_version(self):
      """Determines if the framework corresponds to an and using MMS.
  
      Whether the framework version corresponds to an inference image using
      the Multi-Model Server (https://github.com/awslabs/multi-model-server).
  
      Returns:
          bool: If the framework version corresponds to an image using MMS.
      """
      lowest_mms_version = packaging.version.Version(
          self._LOWEST_MMS_VERSION
      )
      framework_version = packaging.version.Version(
          self.framework_version
      )
      return framework_version >= lowest_mms_version
  
imports: from __future__ import absolute_import
  import logging
  from typing import Optional, Union, List, Dict
  import packaging.version
  import sagemaker
  from sagemaker import image_uris, ModelMetrics
  from sagemaker.deserializers import NumpyDeserializer
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.fw_utils import model_code_key_prefix, python_deprecation_warning, validate_version_or_image_args
  from sagemaker.metadata_properties import MetadataProperties
  from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
  from sagemaker.pytorch import defaults
  from sagemaker.predictor import Predictor
  from sagemaker.serializers import NumpySerializer
  from sagemaker.utils import to_string
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 5 for calling the PyTorchModel obj's underlying member method code with example usage: obj._is_mms_version() and returns: unknown