code: def check_model(model: bentoml.Model, resource_cfg: dict[str, t.Any]):
      from bentoml._internal.resource import get_resource
  
      if get_resource(resource_cfg, "nvidia.com/gpu"):
          pass
      elif get_resource(resource_cfg, "cpu"):
          cpus = round(get_resource(resource_cfg, "cpu"))
          assert model._providers == ["CPUExecutionProvider"]
          assert model._sess_options.inter_op_num_threads == cpus
          assert model._sess_options.intra_op_num_threads == cpus
  
imports: from __future__ import annotations
  import os
  import tempfile
  import typing
  from typing import TYPE_CHECKING
  import numpy
  import onnx
  import onnxruntime
  import sklearn
  import torch
  import torch.nn
  from skl2onnx import convert_sklearn
  from skl2onnx.common.data_types import FloatTensorType
  from skl2onnx.common.data_types import Int64TensorType
  from skl2onnx.common.data_types import StringTensorType
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import LabelEncoder
  import bentoml
  from .None import FrameworkTestModel
  from .None import FrameworkTestModelConfiguration
  from .None import FrameworkTestModelInput
label_desc: Use image node_id 2 for calling a global function with example usage: check_model(model, resource_cfg) without return types