code: def infer(model: nn.Module, ids: List[int], device: torch.device):
      """
      ### Predict the next token
  
      :param model: is the model
      :param ids: are the input token ids
      :param device: is the device of the model
      """
  
      with torch.no_grad():
          # Get the tokens
          x = torch.tensor(ids)[None, :].to(device)
          # Eval model
          x = model(x)
  
      # Return predicted token
      return x[0].max(dim=-1)[1].tolist()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/neox/samples/generate.py
imports: from typing import List
  import torch
  from torch import nn
  from labml import monit
  from labml_nn.neox.model import LayerGenerator
  from labml_nn.neox.utils import get_tokens, print_tokens
  from labml_nn.neox.utils.cache import get_cache
label_desc: Use image node_id 1 for calling a global function with example usage: infer(model, ids, device) and returns: x