code: def construct_scriptable_recurrent(
      cfg_arch, vocab_size, downstream_classes=None
  ):
      """See the config file for details on what is possible."""
      config = crammedRecurrentConfig(
          OmegaConf.to_container(cfg_arch, resolve=True)
      )
      config.embedding.vocab_size = vocab_size
      config.num_labels = downstream_classes
  
      if downstream_classes is None:
          if config.arch["objective_layout"] == "MLM":
              model = BPTTforPreTraining(config)
          else:
              raise ValueError(
                  f"Invalid layout {config.arch['objective_layout']} of training objective given."
              )
      else:
          raise ValueError("Not yet implemented for 2.0")
      return model
  
imports: import torch
  from typing import Optional
  from random import randrange
  from transformers import PretrainedConfig
  from .components import EmbeddingComponent, AttentionComponent, FFNComponent, PredictionHeadComponent, get_extended_attention_mask, _get_norm_fn, _get_nonlin_fn, _init_module
label_desc: Use image node_id 1 for calling a global function with example usage: construct_scriptable_recurrent(cfg_arch, vocab_size, downstream_classes) and returns: model