code: def create_model(
      num_layers: int,
      num_heads: int,
      ff_dim: int,
      h_dim: int,
      dropout: float,
  ) -> RobertaMLMModel:
      """Create a Bert model with the specified `num_heads`, `ff_dim`,
      `h_dim` and `dropout`
  
      Args:
          num_layers (int):
              The number of layers
          num_heads (int):
              The number of attention heads
          ff_dim (int):
              The intermediate hidden size of
              the feed forward block of the
              transformer
          h_dim (int):
              The hidden dim of the intermediate
              representations of the transformer
          dropout (float):
              The value of dropout to be used.
              Note that we apply the same dropout
              to both the attention layers and the
              FF layers
  
      Returns:
          RobertaMLMModel:
              A Roberta model for MLM task
  
      """
      roberta_config_dict = {
          "attention_probs_dropout_prob": dropout,
          "bos_token_id": 0,
          "eos_token_id": 2,
          "hidden_act": "gelu",
          "hidden_dropout_prob": dropout,
          "hidden_size": h_dim,
          "initializer_range": 0.02,
          "intermediate_size": ff_dim,
          "layer_norm_eps": 1e-05,
          "max_position_embeddings": 514,
          "model_type": "roberta",
          "num_attention_heads": num_heads,
          "num_hidden_layers": num_layers,
          "pad_token_id": 1,
          "type_vocab_size": 1,
          "vocab_size": 50265,
      }
      roberta_config = RobertaConfig.from_dict(roberta_config_dict)
      roberta_encoder = RobertaModel(roberta_config)
      roberta_model = RobertaMLMModel(roberta_config, roberta_encoder)
      return roberta_model
  
imports: import os
  import datetime
  import json
  import pathlib
  import re
  import string
  from functools import partial
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, TypeVar, Union
  import random
  import datasets
  import fire
  import logging
  import loguru
  import numpy
  import pytz
  import sh
  import torch
  import torch.nn
  import deepspeed
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.tensorboard import SummaryWriter
  from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
  from transformers.models.roberta import RobertaConfig, RobertaModel
  from transformers.models.roberta.modeling_roberta import RobertaLMHead, RobertaPreTrainedModel
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 6 for calling a global function with example usage: create_model(num_layers, num_heads, ff_dim, h_dim, dropout) and returns: roberta_model