def initialize(job_addresses=None, num_processes=None, process_id=None):
    """Initialize the distribution system for multi-host/process setting.

    Calling `initialize` will prepare the backend for execution on multi-host
    GPU or TPUs. It should be called before any computations.

    Note that the parameters can also be injected via environment variables,
    which can be better controlled by the launch script at startup time.
    For certain backend that also rely on the environment variables to
    configure, Keras will properly forward them.

    Args:
        job_addresses: string. Comma separated IP addresses for all the jobs
            that will form the whole computation cluster. Note that for JAX
            backend, only the address for job 0 (coodinator) is needed. For
            certain runtime like cloud TPU, this value can be `None`, and the
            backend will figure it out with the TPU environment variables. You
            can also config this value via environment variable
            `KERAS_DISTRIBUTION_JOB_ADDRESSES`.
        num_processes: int. The number of worker/processes that will form the
            whole computation cluster. For certain runtime like cloud TPU, this
            value can be `None`, and the backend will figure it out with the TPU
            environment variables. You can also configure this value via
            environment variable `KERAS_DISTRIBUTION_NUM_PROCESSES`.
        process_id: int. The ID number of the current worker/process. The value
            should be ranged from `0` to `num_processes - 1`. `0` will indicate
            the current worker/process is the master/coordinate job. You can
            also configure this value via environment variable
            `KERAS_DISTRIBUTION_PROCESS_ID`.

        Example:
            Suppose there are two GPU processes, and process 0 is running at
            address `10.0.0.1:1234`, and process 1 is running at address
            `10.0.0.2:2345`. To configure such cluster, you can run

        On process 0:
        ```python
        keras.distribute.initialize(
            job_addresses="10.0.0.1:1234,10.0.0.2:2345",
            num_processes=2,
            process_id=0)
        ```

        On process 1:
        ```python
        keras.distribute.initialize(
            job_addresses="10.0.0.1:1234,10.0.0.2:2345",
            num_processes=2,
            process_id=1)
        ```

        or via the environment variables:
        On process 0:
        ```python
        os.environ[
            "KERAS_DISTRIBUTION_JOB_ADDRESSES"] = "10.0.0.1:1234,10.0.0.2:2345"
        os.environ["KERAS_DISTRIBUTION_NUM_PROCESSES"] = "2"
        os.environ["KERAS_DISTRIBUTION_PROCESS_ID"] = "0"
        keras.distribute.initialize()
        ```

        On process 1:
        ```python
        os.environ[
            "KERAS_DISTRIBUTION_JOB_ADDRESSES"] = "10.0.0.1:1234,10.0.0.2:2345"
        os.environ["KERAS_DISTRIBUTION_NUM_PROCESSES"] = "2"
        os.environ["KERAS_DISTRIBUTION_PROCESS_ID"] = "1"
        keras.distribute.initialize()
        ```

        Also note that for JAX backend, the `job_addresses` can be further
        reduced to just the master/coordinator address, which is
        `10.0.0.1:1234`.
    """
    if (
        job_addresses is None
        and "KERAS_DISTRIBUTION_JOB_ADDRESSES" in os.environ
    ):
        job_addresses = os.environ["KERAS_DISTRIBUTION_JOB_ADDRESSES"]
    if (
        num_processes is None
        and "KERAS_DISTRIBUTION_NUM_PROCESSES" in os.environ
    ):
        num_processes = int(os.environ["KERAS_DISTRIBUTION_NUM_PROCESSES"])
    if process_id is None and "KERAS_DISTRIBUTION_PROCESS_ID" in os.environ:
        process_id = int(os.environ["KERAS_DISTRIBUTION_PROCESS_ID"])
    distribution_lib.initialize(job_addresses, num_processes, process_id)
Initialize the distribution system for multi-host/process setting.

    Calling `initialize` will prepare the backend for execution on multi-host
    GPU or TPUs. It should be called before any computations.

    Note that the parameters can also be injected via environment variables,
    which can be better controlled by the launch script at startup time.
    For certain backend that also rely on the environment variables to
    configure, Keras will properly forward them.

    Args:
        job_addresses: string. Comma separated IP addresses for all the jobs
            that will form the whole computation cluster. Note that for JAX
            backend, only the address for job 0 (coodinator) is needed. For
            certain runtime like cloud TPU, this value can be `None`, and the
            backend will figure it out with the TPU environment variables. You
            can also config this value via environment variable
            `KERAS_DISTRIBUTION_JOB_ADDRESSES`.
        num_processes: int. The number of worker/processes that will form the
            whole computation cluster. For certain runtime like cloud TPU, this
            value can be `None`, and the backend will figure it out with the TPU
            environment variables. You can also configure this value via
            environment variable `KERAS_DISTRIBUTION_NUM_PROCESSES`.
        process_id: int. The ID number of the current worker/process. The value
            should be ranged from `0` to `num_processes - 1`. `0` will indicate
            the current worker/process is the master/coordinate job. You can
            also configure this value via environment variable
            `KERAS_DISTRIBUTION_PROCESS_ID`.

        Example:
            Suppose there are two GPU processes, and process 0 is running at
            address `10.0.0.1:1234`, and process 1 is running at address
            `10.0.0.2:2345`. To configure such cluster, you can run

        On process 0:
        ```python
        keras.distribute.initialize(
            job_addresses="10.0.0.1:1234,10.0.0.2:2345",
            num_processes=2,
            process_id=0)
        ```

        On process 1:
        ```python
        keras.distribute.initialize(
            job_addresses="10.0.0.1:1234,10.0.0.2:2345",
            num_processes=2,
            process_id=1)
        ```

        or via the environment variables:
        On process 0:
        ```python
        os.environ[
            "KERAS_DISTRIBUTION_JOB_ADDRESSES"] = "10.0.0.1:1234,10.0.0.2:2345"
        os.environ["KERAS_DISTRIBUTION_NUM_PROCESSES"] = "2"
        os.environ["KERAS_DISTRIBUTION_PROCESS_ID"] = "0"
        keras.distribute.initialize()
        ```

        On process 1:
        ```python
        os.environ[
            "KERAS_DISTRIBUTION_JOB_ADDRESSES"] = "10.0.0.1:1234,10.0.0.2:2345"
        os.environ["KERAS_DISTRIBUTION_NUM_PROCESSES"] = "2"
        os.environ["KERAS_DISTRIBUTION_PROCESS_ID"] = "1"
        keras.distribute.initialize()
        ```

        Also note that for JAX backend, the `job_addresses` can be further
        reduced to just the master/coordinator address, which is
        `10.0.0.1:1234`.
    
keras/src/distribution/distribution_lib.py