code: def create_endpoint_config(self, name, model_name, initial_instance_count, instance_type,
                                 accelerator_type=None, tags=None, kms_key=None):
          
          LOGGER.info('Creating endpoint-config with name {}'.format(name))
  
          tags = tags or []
  
          request = {
              'EndpointConfigName': name,
              'ProductionVariants': [
                  production_variant(model_name, instance_type, initial_instance_count,
                                     accelerator_type=accelerator_type)
              ],
          }
  
          if tags is not None:
              request['Tags'] = tags
  
          if kms_key is not None:
              request['KmsKeyId'] = kms_key
  
          self.sagemaker_client.create_endpoint_config(**request)
          return name
docs: Create an Amazon SageMaker endpoint configuration.
  
          The endpoint configuration identifies the Amazon SageMaker model (created using the
          ``CreateModel`` API) and the hardware configuration on which to deploy the model. Provide this
          endpoint configuration to the ``CreateEndpoint`` API, which then launches the hardware and deploys the model.
  
          Args:
              name (str): Name of the Amazon SageMaker endpoint configuration to create.
              model_name (str): Name of the Amazon SageMaker ``Model``.
              initial_instance_count (int): Minimum number of EC2 instances to launch. The actual number of
                  active instances for an endpoint at any given time varies due to autoscaling.
              instance_type (str): Type of EC2 instance to launch, for example, 'ml.c4.xlarge'.
              accelerator_type (str): Type of Elastic Inference accelerator to attach to the instance. For example,
                  'ml.eia1.medium'. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
              tags(List[dict[str, str]]): Optional. The list of tags to add to the endpoint config. Example:
                      >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
                      For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
                      /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
  
  
          Returns:
              str: Name of the endpoint point configuration created.