class_docstr: Generates trials from the spec.
  
  Args:
      uuid_prefix: Used in creating the trial name.
      num_samples: Number of samples from distribution
           (same as tune.TuneConfig).
      unresolved_spec: Experiment specification
          that might have unresolved distributions.
      constant_grid_search: Should random variables be sampled
          first before iterating over grid variants (True) or not (False).
      points_to_evaluate: Configurations that will be tried out without sampling.
      lazy_eval: Whether variants should be generated
          lazily or eagerly. This is toggled depending
          on the size of the grid search.
      start: index at which to start counting trials.
      random_state (int | np.random.Generator | np.random.RandomState):
          Seed or numpy random generator to use for reproducible results.
          If None (default), will use the global numpy random generator
          (``np.random``). Please note that full reproducibility cannot
          be guaranteed in a distributed enviroment.
code: def create_trial(self, resolved_vars, spec):
      trial_id = self.uuid_prefix + ("%05d" % self.counter)
      experiment_tag = str(self.counter)
      # Always append resolved vars to experiment tag?
      if resolved_vars:
          experiment_tag += "_{}".format(format_vars(resolved_vars))
      self.counter += 1
      return _create_trial_from_spec(
          spec,
          self.parser,
          evaluated_params=_flatten_resolved_vars(resolved_vars),
          trial_id=trial_id,
          experiment_tag=experiment_tag,
      )
  
imports: import copy
  import glob
  import itertools
  import os
  import uuid
  from typing import Dict, List, Optional, Union, TYPE_CHECKING
  import warnings
  import numpy
  from ray.air._internal.usage import tag_searcher
  from ray.tune.error import TuneError
  from ray.tune.experiment.config_parser import _make_parser, _create_trial_from_spec
  from ray.tune.search.sample import np_random_generator, _BackwardsCompatibleNumpyRng
  from ray.tune.search.variant_generator import _count_variants, _count_spec_samples, generate_variants, format_vars, _flatten_resolved_vars, _get_preset_variants
  from ray.tune.search.search_algorithm import SearchAlgorithm
  from ray.tune.utils.util import _atomic_save, _load_newest_checkpoint
  from ray.util import PublicAPI
label_desc: Use image node_id 2 for calling the _TrialIterator obj's underlying member method code with example usage: obj.create_trial(resolved_vars, spec) and returns: _create_trial_from_spec