code: def predict_fn(data, model_tokenizer_model_config):
      # destruct model, tokenizer and model config
      model, tokenizer, model_config = model_tokenizer_model_config
  
      # create embeddings for inputs
      inputs = data.pop("inputs", data)
      embeddings = tokenizer(
          inputs,
          return_tensors="pt",
          max_length=model_config.traced_sequence_length,
          padding="max_length",
          truncation=True,
      )
      # convert to tuple for neuron model
      neuron_inputs = tuple(embeddings.values())
  
      # run prediciton
      with torch.no_grad():
          predictions = model(*neuron_inputs)[0]
          scores = torch.nn.Softmax(dim=1)(predictions)
  
      # return dictonary, which will be json serializable
      return [
          {
              "label": model_config.id2label[item.argmax().item()],
              "score": item.max().item(),
          }
          for item in scores
      ]
  
imports: import os
  from transformers import AutoConfig, AutoTokenizer
  import torch
  import torch.neuron
label_desc: Use image node_id 2 for calling a global function with example usage: predict_fn(data, model_tokenizer_model_config) and returns: unknown