code: def start_raylet(
      redis_address: str,
      gcs_address: str,
      node_ip_address: str,
      node_manager_port: int,
      raylet_name: str,
      plasma_store_name: str,
      cluster_id: str,
      worker_path: str,
      setup_worker_path: str,
      storage: str,
      temp_dir: str,
      session_dir: str,
      resource_dir: str,
      log_dir: str,
      resource_spec,
      plasma_directory: str,
      object_store_memory: int,
      session_name: str,
      is_head_node: bool,
      min_worker_port: Optional[int] = None,
      max_worker_port: Optional[int] = None,
      worker_port_list: Optional[List[int]] = None,
      object_manager_port: Optional[int] = None,
      redis_password: Optional[str] = None,
      metrics_agent_port: Optional[int] = None,
      metrics_export_port: Optional[int] = None,
      dashboard_agent_listen_port: Optional[int] = None,
      runtime_env_agent_port: Optional[int] = None,
      use_valgrind: bool = False,
      use_profiler: bool = False,
      stdout_file: Optional[str] = None,
      stderr_file: Optional[str] = None,
      config: Optional[dict] = None,
      huge_pages: bool = False,
      fate_share: Optional[bool] = None,
      socket_to_use: Optional[int] = None,
      max_bytes: int = 0,
      backup_count: int = 0,
      ray_debugger_external: bool = False,
      env_updates: Optional[dict] = None,
      node_name: Optional[str] = None,
      webui: Optional[str] = None,
      labels: Optional[dict] = None,
  ):
      """Start a raylet, which is a combined local scheduler and object manager.
  
      Args:
          redis_address: The address of the primary Redis server.
          gcs_address: The address of GCS server.
          node_ip_address: The IP address of this node.
          node_manager_port: The port to use for the node manager. If it's
              0, a random port will be used.
          raylet_name: The name of the raylet socket to create.
          plasma_store_name: The name of the plasma store socket to connect
               to.
          worker_path: The path of the Python file that new worker
              processes will execute.
          setup_worker_path: The path of the Python file that will set up
              the environment for the worker process.
          storage: The persistent storage URI.
          temp_dir: The path of the temporary directory Ray will use.
          session_dir: The path of this session.
          resource_dir: The path of resource of this session .
          log_dir: The path of the dir where log files are created.
          resource_spec: Resources for this raylet.
          session_name: The session name (cluster id) of this cluster.
          object_manager_port: The port to use for the object manager. If this is
              None, then the object manager will choose its own port.
          min_worker_port: The lowest port number that workers will bind
              on. If not set, random ports will be chosen.
          max_worker_port: The highest port number that workers will bind
              on. If set, min_worker_port must also be set.
          redis_password: The password to use when connecting to Redis.
          metrics_agent_port: The port where metrics agent is bound to.
          metrics_export_port: The port at which metrics are exposed to.
          dashboard_agent_listen_port: The port at which the dashboard agent
              listens to for HTTP.
          runtime_env_agent_port: The port at which the runtime env agent
              listens to for HTTP.
          use_valgrind: True if the raylet should be started inside
              of valgrind. If this is True, use_profiler must be False.
          use_profiler: True if the raylet should be started inside
              a profiler. If this is True, use_valgrind must be False.
          stdout_file: A file handle opened for writing to redirect stdout to. If
              no redirection should happen, then this should be None.
          stderr_file: A file handle opened for writing to redirect stderr to. If
              no redirection should happen, then this should be None.
          tracing_startup_hook: Tracing startup hook.
          config: Optional Raylet configuration that will
              override defaults in RayConfig.
          max_bytes: Log rotation parameter. Corresponding to
              RotatingFileHandler's maxBytes.
          backup_count: Log rotation parameter. Corresponding to
              RotatingFileHandler's backupCount.
          ray_debugger_external: True if the Ray debugger should be made
              available externally to this node.
          env_updates: Environment variable overrides.
          labels: The key-value labels of the node.
      Returns:
          ProcessInfo for the process that was started.
      """
      assert (
          node_manager_port is not None
          and type(node_manager_port) == int
      )
  
      if use_valgrind and use_profiler:
          raise ValueError(
              "Cannot use valgrind and profiler at the same time."
          )
  
      assert resource_spec.resolved()
      static_resources = resource_spec.to_resource_dict()
  
      # Limit the number of workers that can be started in parallel by the
      # raylet. However, make sure it is at least 1.
      num_cpus_static = static_resources.get("CPU", 0)
      maximum_startup_concurrency = max(
          1, min(multiprocessing.cpu_count(), num_cpus_static)
      )
  
      # Format the resource argument in a form like 'CPU,1.0,GPU,0,Custom,3'.
      resource_argument = ",".join(
          ["{},{}".format(*kv) for kv in static_resources.items()]
      )
  
      has_java_command = False
      if shutil.which("java") is not None:
          has_java_command = True
  
      ray_java_installed = False
      try:
          jars_dir = get_ray_jars_dir()
          if os.path.exists(jars_dir):
              ray_java_installed = True
      except Exception:
          pass
  
      include_java = has_java_command and ray_java_installed
      if include_java is True:
          java_worker_command = build_java_worker_command(
              gcs_address,
              plasma_store_name,
              raylet_name,
              redis_password,
              session_dir,
              node_ip_address,
              setup_worker_path,
          )
      else:
          java_worker_command = []
  
      if os.path.exists(DEFAULT_WORKER_EXECUTABLE):
          cpp_worker_command = build_cpp_worker_command(
              gcs_address,
              plasma_store_name,
              raylet_name,
              redis_password,
              session_dir,
              log_dir,
              node_ip_address,
              setup_worker_path,
          )
      else:
          cpp_worker_command = []
  
      # Create the command that the Raylet will use to start workers.
      # TODO(architkulkarni): Pipe in setup worker args separately instead of
      # inserting them into start_worker_command and later erasing them if
      # needed.
      start_worker_command = (
          [
              sys.executable,
              setup_worker_path,
          ]
          + _site_flags()  # Inherit "-S" and "-s" flags from current Python interpreter.
          + [
              worker_path,
              f"--node-ip-address={node_ip_address}",
              "--node-manager-port=RAY_NODE_MANAGER_PORT_PLACEHOLDER",
              f"--object-store-name={plasma_store_name}",
              f"--raylet-name={raylet_name}",
              f"--redis-address={redis_address}",
              f"--metrics-agent-port={metrics_agent_port}",
              f"--runtime-env-agent-port={runtime_env_agent_port}",
              f"--logging-rotate-bytes={max_bytes}",
              f"--logging-rotate-backup-count={backup_count}",
              f"--runtime-env-agent-port={runtime_env_agent_port}",
              f"--gcs-address={gcs_address}",
              f"--session-name={session_name}",
              f"--temp-dir={temp_dir}",
              f"--webui={webui}",
              f"--cluster-id={cluster_id}",
          ]
      )
  
      if storage is not None:
          start_worker_command.append(f"--storage={storage}")
  
      start_worker_command.append(
          "RAY_WORKER_DYNAMIC_OPTION_PLACEHOLDER"
      )
  
      if redis_password:
          start_worker_command += [f"--redis-password={redis_password}"]
  
      # If the object manager port is None, then use 0 to cause the object
      # manager to choose its own port.
      if object_manager_port is None:
          object_manager_port = 0
  
      if min_worker_port is None:
          min_worker_port = 0
  
      if max_worker_port is None:
          max_worker_port = 0
  
      labels_json_str = ""
      if labels:
          labels_json_str = json.dumps(labels)
  
      dashboard_agent_command = [
          *_build_python_executable_command_memory_profileable(
              ray_constants.PROCESS_TYPE_DASHBOARD_AGENT, session_dir
          ),
          os.path.join(RAY_PATH, "dashboard", "agent.py"),
          f"--node-ip-address={node_ip_address}",
          f"--metrics-export-port={metrics_export_port}",
          f"--dashboard-agent-port={metrics_agent_port}",
          f"--listen-port={dashboard_agent_listen_port}",
          "--node-manager-port=RAY_NODE_MANAGER_PORT_PLACEHOLDER",
          f"--object-store-name={plasma_store_name}",
          f"--raylet-name={raylet_name}",
          f"--temp-dir={temp_dir}",
          f"--session-dir={session_dir}",
          f"--log-dir={log_dir}",
          f"--logging-rotate-bytes={max_bytes}",
          f"--logging-rotate-backup-count={backup_count}",
          f"--session-name={session_name}",
          f"--gcs-address={gcs_address}",
      ]
      if stdout_file is None and stderr_file is None:
          # If not redirecting logging to files, unset log filename.
          # This will cause log records to go to stderr.
          dashboard_agent_command.append("--logging-filename=")
          # Use stderr log format with the component name as a message prefix.
          logging_format = ray_constants.LOGGER_FORMAT_STDERR.format(
              component=ray_constants.PROCESS_TYPE_DASHBOARD_AGENT
          )
          dashboard_agent_command.append(
              f"--logging-format={logging_format}"
          )
  
      if (
          not ray._private.utils.check_dashboard_dependencies_installed()
      ):
          # If dependencies are not installed, it is the minimally packaged
          # ray. We should restrict the features within dashboard agent
          # that requires additional dependencies to be downloaded.
          dashboard_agent_command.append("--minimal")
  
      runtime_env_agent_command = [
          *_build_python_executable_command_memory_profileable(
              ray_constants.PROCESS_TYPE_RUNTIME_ENV_AGENT, session_dir
          ),
          os.path.join(
              RAY_PATH, "_private", "runtime_env", "agent", "main.py"
          ),
          f"--node-ip-address={node_ip_address}",
          f"--runtime-env-agent-port={runtime_env_agent_port}",
          f"--gcs-address={gcs_address}",
          f"--runtime-env-dir={resource_dir}",
          f"--logging-rotate-bytes={max_bytes}",
          f"--logging-rotate-backup-count={backup_count}",
          f"--log-dir={log_dir}",
          f"--temp-dir={temp_dir}",
      ]
  
      command = [
          RAYLET_EXECUTABLE,
          f"--raylet_socket_name={raylet_name}",
          f"--store_socket_name={plasma_store_name}",
          f"--object_manager_port={object_manager_port}",
          f"--min_worker_port={min_worker_port}",
          f"--max_worker_port={max_worker_port}",
          f"--node_manager_port={node_manager_port}",
          f"--node_ip_address={node_ip_address}",
          f"--maximum_startup_concurrency={maximum_startup_concurrency}",
          f"--static_resource_list={resource_argument}",
          f"--python_worker_command={subprocess.list2cmdline(start_worker_command)}",  # noqa
          f"--java_worker_command={subprocess.list2cmdline(java_worker_command)}",  # noqa
          f"--cpp_worker_command={subprocess.list2cmdline(cpp_worker_command)}",  # noqa
          f"--native_library_path={DEFAULT_NATIVE_LIBRARY_PATH}",
          f"--temp_dir={temp_dir}",
          f"--session_dir={session_dir}",
          f"--log_dir={log_dir}",
          f"--resource_dir={resource_dir}",
          f"--metrics-agent-port={metrics_agent_port}",
          f"--metrics_export_port={metrics_export_port}",
          f"--runtime_env_agent_port={runtime_env_agent_port}",
          f"--object_store_memory={object_store_memory}",
          f"--plasma_directory={plasma_directory}",
          f"--ray-debugger-external={1 if ray_debugger_external else 0}",
          f"--gcs-address={gcs_address}",
          f"--session-name={session_name}",
          f"--labels={labels_json_str}",
          f"--cluster-id={cluster_id}",
      ]
  
      if is_head_node:
          command.append("--head")
  
      if worker_port_list is not None:
          command.append(f"--worker_port_list={worker_port_list}")
      command.append(
          "--num_prestart_python_workers={}".format(
              int(resource_spec.num_cpus)
          )
      )
      command.append(
          "--dashboard_agent_command={}".format(
              subprocess.list2cmdline(dashboard_agent_command)
          )
      )
      command.append(
          "--runtime_env_agent_command={}".format(
              subprocess.list2cmdline(runtime_env_agent_command)
          )
      )
      if huge_pages:
          command.append("--huge_pages")
      if socket_to_use:
          socket_to_use.close()
      if node_name is not None:
          command.append(
              f"--node-name={node_name}",
          )
      process_info = start_ray_process(
          command,
          ray_constants.PROCESS_TYPE_RAYLET,
          use_valgrind=use_valgrind,
          use_gdb=False,
          use_valgrind_profiler=use_profiler,
          use_perftools_profiler=(
              "RAYLET_PERFTOOLS_PATH" in os.environ
          ),
          stdout_file=stdout_file,
          stderr_file=stderr_file,
          fate_share=fate_share,
          env_updates=env_updates,
      )
  
      return process_info
  
imports: import base64
  import collections
  import errno
  import io
  import json
  import logging
  import mmap
  import multiprocessing
  import os
  import random
  import shutil
  import signal
  import socket
  import subprocess
  import sys
  import time
  from pathlib import Path
  from typing import List, Optional, IO, AnyStr
  import psutil
  from filelock import FileLock
  import ray
  import ray._private.ray_constants
  from ray._raylet import GcsClient, GcsClientOptions
  from ray.core.generated.common_pb2 import Language
  from ray._private.ray_constants import RAY_NODE_IP_FILENAME
label_desc: Use image node_id 34 for calling a global function with example usage: start_raylet(redis_address, gcs_address, node_ip_address, node_manager_port, raylet_name, plasma_store_name, cluster_id, worker_path, setup_worker_path, storage, temp_dir, session_dir, resource_dir, log_dir, resource_spec, plasma_directory, object_store_memory, session_name, is_head_node, min_worker_port, max_worker_port, worker_port_list, object_manager_port, redis_password, metrics_agent_port, metrics_export_port, dashboard_agent_listen_port, runtime_env_agent_port, use_valgrind, use_profiler, stdout_file, stderr_file, config, huge_pages, fate_share, socket_to_use, max_bytes, backup_count, ray_debugger_external, env_updates, node_name, webui, labels) and returns: process_info