code: def construct_crammed_bert(
      cfg_arch, vocab_size, downstream_classes=None
  ):
      """See the config file for details on what is possible."""
      config = crammedBertConfig(
          OmegaConf.to_container(cfg_arch, resolve=True)
      )
      config.arch["embedding"]["vocab_size"] = vocab_size
      config.arch["num_labels"] = downstream_classes
  
      if downstream_classes is None:
          if config.arch["objective_layout"] == "MLM":
              model = ScriptableLMForPreTraining(config)
          elif config.arch["objective_layout"] == "SCRIPT":
              model = ScriptableLMForSCRIPTTraining(config)
          else:
              raise ValueError(
                  f"Invalid layout {config.arch['objective_layout']} of training objective given."
              )
      else:
          model = ScriptableLMForSequenceClassification(config)
      return model
  
imports: import torch
  from transformers import PretrainedConfig, PreTrainedModel
  from transformers import AutoConfig, AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification
  from typing import Optional
  from omegaconf import OmegaConf
  from .fff import FFF
  from .components import _get_norm_fn, _get_nonlin_fn, EmbeddingComponent, PoolingComponent, PredictionHeadComponent, GLU, get_extended_attention_mask, _init_module
  from .attention import get_attention_mechanism
label_desc: Use image node_id 1 for calling a global function with example usage: construct_crammed_bert(cfg_arch, vocab_size, downstream_classes) and returns: model