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 deployment with the specified name. You can update the URI of the model, the
  flavor of the deployed model (in which case the model URI must also be specified), and/or
  any target-specific attributes of the deployment (via `config`). By default, this method
  should block until deployment completes (i.e. until it's possible to perform inference
  with the updated deployment). See target-specific plugin documentation for additional
  detail on support for asynchronous deployment and other configuration.
  
  Args:
      name: Unique name of deployment to update.
      model_uri: URI of a new model to deploy.
      flavor: (optional) new model flavor to use for deployment. If provided,
          ``model_uri`` must also be specified. If ``flavor`` is unspecified but
          ``model_uri`` is specified, a default flavor will be chosen and the
          deployment will be updated using that flavor.
      config: (optional) dict containing updated target-specific configuration for the
          deployment.
      endpoint: (optional) Endpoint containing the deployment to update. May not be
          supported by all targets.
  
  Returns:
      None
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code: def update_deployment(
      self,
      name,
      model_uri=None,
      flavor=None,
      config=None,
      endpoint=None,
  ):
      """
      Update the deployment with the specified name. You can update the URI of the model, the
      flavor of the deployed model (in which case the model URI must also be specified), and/or
      any target-specific attributes of the deployment (via `config`). By default, this method
      should block until deployment completes (i.e. until it's possible to perform inference
      with the updated deployment). See target-specific plugin documentation for additional
      detail on support for asynchronous deployment and other configuration.
  
      Args:
          name: Unique name of deployment to update.
          model_uri: URI of a new model to deploy.
          flavor: (optional) new model flavor to use for deployment. If provided,
              ``model_uri`` must also be specified. If ``flavor`` is unspecified but
              ``model_uri`` is specified, a default flavor will be chosen and the
              deployment will be updated using that flavor.
          config: (optional) dict containing updated target-specific configuration for the
              deployment.
          endpoint: (optional) Endpoint containing the deployment to update. May not be
              supported by all targets.
  
      Returns:
          None
  
      """
      pass
  
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 3 for calling the BaseDeploymentClient obj's underlying member method code with example usage: obj.update_deployment(name, model_uri, flavor, config, endpoint) without return types