code: def get_transformer(device: torch.device) -> GetterReturnType:
      # For most SOTA research, you would like to have embed to 720, nhead to 12, bsz to 64, tgt_len/src_len to 128.
      N = 64
      seq_length = 128
      ntoken = 50
      model = models.TransformerModel(
          ntoken=ntoken, ninp=720, nhead=12, nhid=2048, nlayers=2
      )
      model.to(device)
  
      if has_functorch:
          # disable dropout for consistency checking
          model.eval()
  
      criterion = nn.NLLLoss()
      params, names = extract_weights(model)
  
      data = (
          torch.rand(N, seq_length + 1, device=device)
          .mul(ntoken)
          .long()
      )
      inputs = data.narrow(1, 0, seq_length)
      targets = data.narrow(1, 1, seq_length)
  
      def forward(*new_params: Tensor) -> Tensor:
          load_weights(model, names, new_params)
          out = model(inputs)
  
          loss = criterion(
              out.reshape(N * seq_length, ntoken),
              targets.reshape(N * seq_length),
          )
          return loss
  
      return forward, params
  
file_path: pytorch/benchmarks/functional_autograd_benchmark/audio_text_models.py
imports: import torch
  import torchaudio_models
  from torch import nn, Tensor
  from utils import check_for_functorch, extract_weights, GetterReturnType, load_weights
label_desc: Use image node_id 3 for calling a global function with example usage: get_transformer(device) and returns: forward,  params, loss