code: def tpu_accelerator_config_to_type(accelerator_config: dict) -> str:
      """Convert a provided accelerator_config to accelerator_type.
  
      Args:
          accelerator_config: A dictionary defining the spec of a
              TPU accelerator. The dictionary should consist of
              the keys 'type', indicating the TPU chip type, and
              'topology', indicating the topology of the TPU.
  
      Returns:
          A string, accelerator_type, e.g. "v4-8".
  
      """
      generation = accelerator_config["type"].lower()
      topology = accelerator_config["topology"]
      # Reduce e.g. "2x2x2" to 8
      chip_dimensions = [
          int(chip_count) for chip_count in topology.split("x")
      ]
      num_chips = reduce(lambda x, y: x * y, chip_dimensions)
      num_cores = num_chips * 2
  
      return f"{generation}-{num_cores}"
  
imports: import copy
  import json
  import logging
  import os
  import re
  import time
  from functools import partial, reduce
  from cryptography.hazmat.backends import default_backend
  from cryptography.hazmat.primitives import serialization
  from cryptography.hazmat.primitives.asymmetric import rsa
  from google.oauth2 import service_account
  from google.oauth2.credentials import Credentials
  from googleapiclient import discovery, errors
  from ray._private.accelerators import TPUAcceleratorManager, tpu
  from ray.autoscaler._private.gcp.node import MAX_POLLS, POLL_INTERVAL, GCPNodeType
  from ray.autoscaler._private.util import check_legacy_fields
label_desc: Use image node_id 1 for calling a global function with example usage: tpu_accelerator_config_to_type(accelerator_config) and returns: generation, str, num_cores