class_docstr: Base class exposing Python model deployment APIs.
  
  Plugin implementors should define target-specific deployment logic via a subclass of
  ``BaseDeploymentClient`` within the plugin module, and customize method docstrings with
  target-specific information.
  
  .. Note::
      Subclasses should raise :py:class:`mlflow.exceptions.MlflowException` in error cases (e.g.
      on failure to deploy a model).
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docstr: Update the endpoint with the specified name. You can update any target-specific attributes
  of the endpoint (via `config`). By default, this method should block until the update
  completes (i.e. until it's possible to create a deployment within the endpoint). See
  target-specific plugin documentation for additional detail on support for asynchronous
  update and other configuration.
  
  Args:
      endpoint: Unique name of endpoint to update
      config: (optional) dict containing target-specific configuration for the
          endpoint
  
  Returns:
      None
docstr_tok: ["Update","the","endpoint","with","the","specified","name",".","You","can","update","any","target-specific","attributes","of","the","endpoint","(","via","`","config","`",")",".","By","default",",","this","method","should","block","until","the","update","completes","(","i.e",".","until","it","'s","possible","to","create","a","deployment","within","the","endpoint",")",".","See","target-specific","plugin","documentation","for","additional","detail","on","support","for","asynchronous","update","and","other","configuration",".","Args",":","endpoint",":","Unique","name","of","endpoint","to","update","config",":","(","optional",")","dict","containing","target-specific","configuration","for","the","endpoint","Returns",":","None"]
code: def update_endpoint(self, endpoint, config=None):
      """
      Update the endpoint with the specified name. You can update any target-specific attributes
      of the endpoint (via `config`). By default, this method should block until the update
      completes (i.e. until it's possible to create a deployment within the endpoint). See
      target-specific plugin documentation for additional detail on support for asynchronous
      update and other configuration.
  
      Args:
          endpoint: Unique name of endpoint to update
          config: (optional) dict containing target-specific configuration for the
              endpoint
  
      Returns:
          None
  
      """
      raise MlflowException(
          "Method is unimplemented in base client. Implementation should be "
          "provided by specific target plugins."
      )
  
file_path: mlflow/mlflow/deployments/base.py
imports: import abc
  from mlflow.exceptions import MlflowException
  from mlflow.utils.annotations import developer_stable
label_desc: Use image node_id 10 for calling the BaseDeploymentClient obj's underlying member method code with example usage: obj.update_endpoint(endpoint, config) without return types