code: def query_nb101_trial_stats(
      arch,
      num_epochs,
      isomorphism=True,
      reduction=None,
      include_intermediates=False,
  ):
      """
      Query trial stats of NAS-Bench-101 given conditions.
  
      Parameters
      ----------
      arch : dict or None
          If a dict, it is in the format that is described in
          :class:`nni.nas.benchmark.nasbench101.Nb101TrialConfig`. Only trial stats
          matched will be returned. If none, all architectures in the database will be matched.
      num_epochs : int or None
          If int, matching results will be returned. Otherwise a wildcard.
      isomorphism : boolean
          Whether to match essentially-same architecture, i.e., architecture with the
          same graph-invariant hash value.
      reduction : str or None
          If 'none' or None, all trial stats will be returned directly.
          If 'mean', fields in trial stats will be averaged given the same trial config.
      include_intermediates : boolean
          If true, intermediate results will be returned.
  
      Returns
      -------
      generator of dict
          A generator of :class:`nni.nas.benchmark.nasbench101.Nb101TrialStats` objects,
          where each of them has been converted into a dict.
      """
  
      if proxy.obj is None:
          proxy.initialize(load_benchmark("nasbench101"))
  
      fields = []
      if reduction == "none":
          reduction = None
      if reduction == "mean":
          for field_name in Nb101TrialStats._meta.sorted_field_names:
              if field_name not in ["id", "config"]:
                  fields.append(
                      fn.AVG(
                          getattr(Nb101TrialStats, field_name)
                      ).alias(field_name)
                  )
      elif reduction is None:
          fields.append(Nb101TrialStats)
      else:
          raise ValueError("Unsupported reduction: '%s'" % reduction)
      query = Nb101TrialStats.select(*fields, Nb101TrialConfig).join(
          Nb101TrialConfig
      )
      conditions = []
      if arch is not None:
          if isomorphism:
              num_vertices = infer_num_vertices(arch)
              conditions.append(
                  Nb101TrialConfig.hash
                  == hash_module(arch, num_vertices)
              )
          else:
              conditions.append(Nb101TrialConfig.arch == arch)
      if num_epochs is not None:
          conditions.append(Nb101TrialConfig.num_epochs == num_epochs)
      if conditions:
          query = query.where(
              functools.reduce(lambda a, b: a & b, conditions)
          )
      if reduction is not None:
          query = query.group_by(Nb101TrialStats.config)
      for trial in query:
          if include_intermediates:
              data = model_to_dict(trial)
              # exclude 'trial' from intermediates as it is already available in data
              data["intermediates"] = [
                  {
                      k: v
                      for k, v in model_to_dict(t).items()
                      if k != "trial"
                  }
                  for t in trial.intermediates
              ]
              yield data
          else:
              yield model_to_dict(trial)
  
imports: import functools
  from peewee import fn
  from playhouse.shortcuts import model_to_dict
  from nni.nas.benchmark.utils import load_benchmark
  from .schema import Nb101TrialStats, Nb101TrialConfig, proxy
  from .graph_util import hash_module, infer_num_vertices
label_desc: Use image node_id 1 for calling a global function with example usage: query_nb101_trial_stats(arch, num_epochs, isomorphism, reduction, include_intermediates) without return types