code: def artificial(
      config: str,
  ):
      """
      Generate artificial datasets specified in a config file.
  
      Parameters
      ----------
      config: str
          Path to config file.
      """
      with Path(config).open() as f:
          options = yaml.safe_load(f)
  
      assert len(options["dm_name"]) == 1, len(options["dm_name"])
      dm_name = options["dm_name"][0]
      prefix = f"{dm_name}_"
  
      data_configs = {}
      for key in options:
          if key in CheatMetaData.__annotations__.keys():
              data_configs[key] = options[key]
          elif key.startswith(prefix):
              data_configs[key[len(prefix) :]] = options[key]
  
      values = list(itertools.product(*data_configs.values()))
      all_configurations = [
          dict(zip(data_configs.keys(), v)) for v in values
      ]
  
      for config in all_configurations:
          dm = get_data_module(
              name=dm_name,
              **config,
          )
          dm.setup()
  
file_path: gluonts/src/gluonts/nursery/few_shot_prediction/src/scripts/data.py
imports: import os
  from pathlib import Path
  import itertools
  import yaml
  from tqdm.auto import tqdm
  import matplotlib.pyplot
  import numpy
  import click
  import torch
  from meta.datasets import get_data_module, DATASETS_FILTERED
  from meta.datasets.cheat import CheatMetaData
  from meta.common.torch import tensor_to_np
  import pandas
label_desc: Use image node_id 3 for calling a global function with example usage: artificial(config) without return types