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: Runs through all the packages that has the `group_name` defined as the entrypoint
  and register that into the registry
docstr_tok: ["Runs","through","all","the","packages","that","has","the","`","group_name","`","defined","as","the","entrypoint","and","register","that","into","the","registry"]
code: def register_entrypoints(self):
      """
      Runs through all the packages that has the `group_name` defined as the entrypoint
      and register that into the registry
      """
      for entrypoint in entrypoints.get_group_all(self.group_name):
          self.registry[entrypoint.name] = entrypoint
      self._has_registered = True
  
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 6 for calling the PluginManager obj's underlying member method code with example usage: obj.register_entrypoints() without return types