code: def initialize(
      job_addresses=None, num_processes=None, proceed_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 enviornment variables,
      which can be better controlled by the launch script at startup time.
      For certain backend that also rely on the enviornment 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 enviornment variables. You
              can also config this value via enviornment 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
              enviornment variables. You can also configure this value via
              enviornment 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 enviornment 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 enviornment 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 (
          proceed_id is None
          and "KERAS_DISTRIBUTION_PROCESS_ID" in os.environ
      ):
          proceed_id = int(os.environ["KERAS_DISTRIBUTION_PROCESS_ID"])
      distribution_lib.initialize(
          job_addresses, num_processes, proceed_id
      )
  
imports: import collections
  import contextlib
  import os
  import re
  import warnings
  import numpy
  from keras.api_export import keras_export
  from keras.backend import KerasTensor
  from keras.backend import distribution_lib
  from keras.backend.common import global_state
label_desc: Use image node_id 2 for calling a global function with example usage: initialize(job_addresses, num_processes, proceed_id) without return types