class_docstr: This components uses SeldonIO MLServer library.
  
  The model endpoint: /v2/models/{MODEL_NAME}/versions/{VERSION}/infer.
  
  Arguments:
      name: The name of the model for the endpoint.
      implementation: The model loader class.
          Example: "mlserver_sklearn.SKLearnModel".
          Learn more here: $ML_SERVER_URL/tree/master/runtimes
      workers: Number of server worker.
code: def __init__(
      self,
      name: str,
      implementation: str,
      workers: int = 1,
      **kwargs,
  ):
      super().__init__(
          parallel=True,
          cloud_build_config=BuildConfig(
              requirements=["mlserver", "mlserver-sklearn"],
          ),
          **kwargs,
      )
      # 1: Collect the config's.
      self.settings = {
          "debug": True,
          "parallel_workers": workers,
      }
      self.model_settings = {
          "name": name,
          "implementation": implementation,
      }
      # 2: Keep track of latest version
      self.version = 1
  
file_path: pytorch-lightning/docs/source-app/examples/model_server_app/model_server.py
imports: import json
  import subprocess
  from lightning import BuildConfig, LightningWork
  from lightning.app.storage.path import Path
label_desc: Use image node_id 1 to create a new MLServer object from inherited base classes: LightningWork with example: obj = MLServer(name, implementation, workers)