code: def _load_model_and_tokenizer(
      name, device, architectures=("Generation", "LMHead", "CausalLM")
  ):
      """Load a transformers model based only on its name
  
      This is a bit tricky, because we usually require the task as well to load
      the correct model. However, asking users to pass name and task is not very
      user friendly because it makes, for instance, defining grid search
      parameters more cumbersome if two parameters need to be changed together.
  
      To solve this, we basically guess the correct architecture based on its
      name.
  
      Parameters
      ----------
      name : str
        The name of the transformers model to load, e.g. ``google/flan-t5-small``.
  
      device : str or torch.device
        The device to use for the model, e.g. ``'cpu'`` or
        ``torch.device('cuda:0')``.
  
      architectures : tuple of str (default=('Generation', 'LMHead', 'CausalLM'))
        The architectures allowed to be loaded. An architecture is chosen if one
        of the strings is a substring of the actual architecture name.
  
      Returns
      -------
      model : torch.nn.Module
        The pre-trained transformers model.
  
      tokenizer
        The tokenizer for the model.
  
      Raises
      ------
      ValueError
        If the model architecture cannot be inferred from the name, raise a
        ``ValueError``.
  
      """
      config = AutoConfig.from_pretrained(name)
  
      architecture = None
      for arch in config.architectures:
          if any(substr in arch for substr in architectures):
              architecture = arch
              break
  
      if architecture is None:
          raise ValueError(
              f"Could not identify architecture for model '{name}', try loading "
              "model and tokenizer directly using the corresponding 'Auto' classes "
              "from transformers and pass them to the classifier"
          )
  
      transformers_module = importlib.import_module("transformers")
      cls = getattr(transformers_module, architecture, None)
      if cls is None:
          raise ValueError(
              f"Could not find a class '{architecture}' in transformers, try loading "
              "model and tokenizer directly using the corresponding 'Auto' classes "
              "from transformers and pass them to the classifier"
          )
  
      model = cls.from_pretrained(name).to(device)
      tokenizer = AutoTokenizer.from_pretrained(name)
      return model, tokenizer
  
imports: import importlib
  import warnings
  from string import Formatter
  import numpy
  import torch
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.utils import check_random_state
  from sklearn.utils.validation import check_is_fitted
  from transformers import AutoConfig, AutoTokenizer, LogitsProcessor
  from skorch.exceptions import LowProbabilityError
  from skorch.llm.prompts import DEFAULT_PROMPT_FEW_SHOT, DEFAULT_PROMPT_ZERO_SHOT, DELIM
label_desc: Use image node_id 2 for calling a global function with example usage: _load_model_and_tokenizer(name, device, architectures) and returns: model,  tokenizer