Repository: jina-ai/jina
File: jina/orchestrate/flow/base.py (Python)

Code before:
        **kwargs,
    ) -> Union['Flow', 'AsyncFlow']:
        """Add an Executor to the current Flow object.

        :param compression: The compression mechanism used when sending requests from the Head to the WorkerRuntimes. For more details, check https://grpc.github.io/grpc/python/grpc.html#compression.
        :param connection_list: dictionary JSON with a list of connections to configure
        :param disable_auto_volume: Do not automatically mount a volume for dockerized Executors.
        :param disable_reduce: Disable the built-in reduce mechanism, set this if the reduction is to be handled by the Executor connected to this Head
        :param docker_kwargs: Dictionary of kwargs arguments that will be passed to Docker SDK when starting the docker '
          container.

          More details can be found in the Docker SDK docs:  https://docker-py.readthedocs.io/en/stable/
        :param entrypoint: The entrypoint command overrides the ENTRYPOINT in Docker image. when not set then the Docker image ENTRYPOINT takes effective.
        :param env: The map of environment variables that are available inside runtime
        :param external: The Deployment will be considered an external Deployment that has been started independently from the Flow.This Deployment will not be context managed by the Flow.
        :param floating: If set, the current Pod/Deployment can not be further chained, and the next `.add()` will chain after the last Pod/Deployment not this current one.
        :param force_update: If set, always pull the latest Hub Executor bundle even it exists on local
        :param gpus: This argument allows dockerized Jina executor discover local gpu devices.

              Note,
              - To access all gpus, use `--gpus all`.
              - To access multiple gpus, e.g. make use of 2 gpus, use `--gpus 2`.
              - To access specified gpus based on device id, use `--gpus device=[YOUR-GPU-DEVICE-ID]`
              - To access specified gpus based on multiple device id, use `--gpus device=[YOUR-GPU-DEVICE-ID1],device=[YOUR-GPU-DEVICE-ID2]`
              - To specify more parameters, use `--gpus device=[YOUR-GPU-DEVICE-ID],runtime=nvidia,capabilities=display
        :param grpc_server_options: Dictionary of kwargs arguments that will be passed to the grpc server as options when starting the server, example : {grpc.max_send_message_length': -1}
        :param host: The host address of the runtime, by default it is 0.0.0.0.
        :param host_in: The host address for binding to, by default it is 0.0.0.0
        :param install_requirements: If set, install `requirements.txt` in the Hub Executor bundle to local
        :param log_config: The YAML config of the logger used in this object.
        :param monitoring: If set, spawn an http server with a prometheus endpoint to expose metrics
        :param name: The name of this object.

              This will be used in the following places:
              - how you refer to this object in Python/YAML/CLI
              - visualization
              - log message header
              - ...

              When not given, then the default naming strategy will apply.
        :param native: If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.
        :param output_array_type: The type of array `tensor` and `embedding` will be serialized to.

          Supports the same types as `docarray.to_protobuf(.., ndarray_type=...)`, which can be found
          `here <https://docarray.jina.ai/fundamentals/document/serialization/#from-to-protobuf>`.
          Defaults to retaining whatever type is returned by the Executor.
        :param polling: The polling strategy of the Deployment and its endpoints (when `shards>1`).
              Can be defined for all endpoints of a Deployment or by endpoint.
              Define per Deployment:
              - ANY: only one (whoever is idle) Pod polls the message
              - ALL: all Pods poll the message (like a broadcast)

Review comment (suggestion):
```suggestion
        :param grpc_server_options: Dictionary of kwargs arguments that will be passed to the grpc server as options when starting the server, example : {'grpc.max_send_message_length': -1}
```

Code after:
        **kwargs,
    ) -> Union['Flow', 'AsyncFlow']:
        """Add an Executor to the current Flow object.

        :param compression: The compression mechanism used when sending requests from the Head to the WorkerRuntimes. For more details, check https://grpc.github.io/grpc/python/grpc.html#compression.
        :param connection_list: dictionary JSON with a list of connections to configure
        :param disable_auto_volume: Do not automatically mount a volume for dockerized Executors.
        :param disable_reduce: Disable the built-in reduce mechanism, set this if the reduction is to be handled by the Executor connected to this Head
        :param docker_kwargs: Dictionary of kwargs arguments that will be passed to Docker SDK when starting the docker '
          container.

          More details can be found in the Docker SDK docs:  https://docker-py.readthedocs.io/en/stable/
        :param entrypoint: The entrypoint command overrides the ENTRYPOINT in Docker image. when not set then the Docker image ENTRYPOINT takes effective.
        :param env: The map of environment variables that are available inside runtime
        :param external: The Deployment will be considered an external Deployment that has been started independently from the Flow.This Deployment will not be context managed by the Flow.
        :param floating: If set, the current Pod/Deployment can not be further chained, and the next `.add()` will chain after the last Pod/Deployment not this current one.
        :param force_update: If set, always pull the latest Hub Executor bundle even it exists on local
        :param gpus: This argument allows dockerized Jina executor discover local gpu devices.

              Note,
              - To access all gpus, use `--gpus all`.
              - To access multiple gpus, e.g. make use of 2 gpus, use `--gpus 2`.
              - To access specified gpus based on device id, use `--gpus device=[YOUR-GPU-DEVICE-ID]`
              - To access specified gpus based on multiple device id, use `--gpus device=[YOUR-GPU-DEVICE-ID1],device=[YOUR-GPU-DEVICE-ID2]`
              - To specify more parameters, use `--gpus device=[YOUR-GPU-DEVICE-ID],runtime=nvidia,capabilities=display
        :param grpc_server_options: Dictionary of kwargs arguments that will be passed to the grpc server as options when starting the server, example : {'grpc.max_send_message_length': -1}
        :param host: The host address of the runtime, by default it is 0.0.0.0.
        :param host_in: The host address for binding to, by default it is 0.0.0.0
        :param install_requirements: If set, install `requirements.txt` in the Hub Executor bundle to local
        :param log_config: The YAML config of the logger used in this object.
        :param monitoring: If set, spawn an http server with a prometheus endpoint to expose metrics
        :param name: The name of this object.

              This will be used in the following places:
              - how you refer to this object in Python/YAML/CLI
              - visualization
              - log message header
              - ...

              When not given, then the default naming strategy will apply.
        :param native: If set, only native Executors is allowed, and the Executor is always run inside WorkerRuntime.
        :param output_array_type: The type of array `tensor` and `embedding` will be serialized to.

          Supports the same types as `docarray.to_protobuf(.., ndarray_type=...)`, which can be found
          `here <https://docarray.jina.ai/fundamentals/document/serialization/#from-to-protobuf>`.
          Defaults to retaining whatever type is returned by the Executor.
        :param polling: The polling strategy of the Deployment and its endpoints (when `shards>1`).
              Can be defined for all endpoints of a Deployment or by endpoint.
              Define per Deployment:
              - ANY: only one (whoever is idle) Pod polls the message
              - ALL: all Pods poll the message (like a broadcast)