class_docstr: Abstract class defining a entrypoint based plugin registration.
  
  This class allows the registration of a function or class to provide an implementation
  for a given key/name. Implementations declared though the entrypoints can be automatically
  registered through the `register_entrypoints` method.
class_docstr_tok: ["Abstract","class","defining","a","entrypoint","based","plugin","registration",".","This","class","allows","the","registration","of","a","function","or","class","to","provide","an","implementation","for","a","given","key\/name",".","Implementations","declared","though","the","entrypoints","can","be","automatically","registered","through","the","`","register_entrypoints","`","method","."]
docstr: Registry stores the registered plugin as a key value pair where key is the
  name of the plugin and value is the plugin object
docstr_tok: ["Registry","stores","the","registered","plugin","as","a","key","value","pair","where","key","is","the","name","of","the","plugin","and","value","is","the","plugin","object"]
code: def registry(self):
      """
      Registry stores the registered plugin as a key value pair where key is the
      name of the plugin and value is the plugin object
      """
      return self._registry
  
file_path: mlflow/mlflow/deployments/plugin_manager.py
imports: import abc
  import inspect
  import entrypoints
  from mlflow.deployments.base import BaseDeploymentClient
  from mlflow.deployments.utils import parse_target_uri
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, RESOURCE_DOES_NOT_EXIST
  from mlflow.utils.annotations import developer_stable
label_desc: Use image node_id 3 for calling the PluginManager obj's underlying member method code with example usage: obj.registry() and returns: self