class_docstr: cluster
  
  This is meant as a thin wrapper around GKE clusters and the raw kubernetes
  python api, mainly to provide a simple interface for the most common
  cluster tasks.
class_docstr_tok: ["cluster","This","is","meant","as","a","thin","wrapper","around","GKE","clusters","and","the","raw","kubernetes","python","api",",","mainly","to","provide","a","simple","interface","for","the","most","common","cluster","tasks","."]
docstr: validates gpu spec against zone and cluster contraints
  
  Args
  gpu_spec: gpu spec
  
  Returns:
  True if valid spec, False otherwise
docstr_tok: ["validates","gpu","spec","against","zone","and","cluster","contraints","Args","gpu_spec",":","gpu","spec","Returns",":","True","if","valid","spec",",","False","otherwise"]
code: def validate_gpu_spec(self, gpu_spec: Optional[GPUSpec]) -> bool:
      """validates gpu spec against zone and cluster contraints
  
      Args
      gpu_spec: gpu spec
  
      Returns:
      True if valid spec, False otherwise
      """
      if gpu_spec is None:
          return True
  
      # ------------------------------------------------------------------------
      # validate against zone instance limits
      compute_api = googleapiclient.discovery.build(
          "compute",
          "v1",
          credentials=self.credentials,
          cache_discovery=False,
      )
  
      zone_gpus = util.get_zone_gpu_types(
          compute_api, self.project_id, self.zone
      )
  
      if zone_gpus is None:
          return False
  
      gpu_limits = dict([(x.gpu, x.count) for x in zone_gpus])
      if not util.validate_gpu_spec_against_limits(
          gpu_spec, gpu_limits, "zone"
      ):
          return False
  
      # ------------------------------------------------------------------------
      # validate against cluster limits
      available_gpu = self.get_gpu_types()
      if available_gpu is None:
          return False
  
      gpu_limits = dict([(x.gpu, x.count) for x in available_gpu])
      if not util.validate_gpu_spec_against_limits(
          gpu_spec, gpu_limits, "cluster"
      ):
          return False
  
      return True
  
imports: import json
  import logging
  import re
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
  import googleapiclient
  import kubernetes
  import requests
  import urllib3
  import yaml
  from google.auth.credentials import Credentials
  from google.cloud.container_v1 import ClusterManagerClient
  from google.cloud.container_v1.types import NodePool, Cluster
  from googleapiclient import discovery
  from googleapiclient.http import HttpRequest
  from kubernetes.client import V1Container, V1DaemonSet, V1EnvVar, V1Job, V1JobSpec, V1ObjectMeta, V1Pod, V1PodSpec, V1PodTemplateSpec, V1ResourceRequirements, V1Toleration
  from kubernetes.client.api_client import ApiClient
  import caliban.config.experiment
  import caliban.platform.gke.constants
  import caliban.platform.gke.util
  from caliban.history.types import Experiment, Job, JobSpec, JobStatus, Platform
  from caliban.platform.cloud.types import GPU, TPU, Accelerator, GPUSpec, MachineType, TPUSpec
  from caliban.platform.gke.types import NodeImage, OpStatus, ReleaseChannel
  from caliban.platform.gke.util import trap
  import caliban.util.metrics
label_desc: Use image node_id 27 for calling the Cluster obj's underlying member method code with example usage: obj.validate_gpu_spec(gpu_spec) and returns: True, True, False, False, False, False