code: def forward(
      self,
      input_ids,
      attention_mask,
      position_ids,
      past_key_values: Optional = None,
  ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
      # Model Forward
      kwargs = {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "past_key_values": past_key_values,
          "use_cache": True,
          "return_dict": True,
      }
      if self.has_position_ids:
          kwargs["position_ids"] = position_ids
  
      outputs = self.model.forward(**kwargs)
      return outputs.logits, outputs.past_key_values
  
imports: import torch
  import time
  from dataclasses import dataclass
  from opentelemetry import trace
  from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
  from typing import Optional, Tuple, List, Type, Dict
  from text_generation_server.models import Model
  from text_generation_server.utils.tokens import batch_top_tokens
  from text_generation_server.models.types import Batch, Tokens, Generation, GeneratedText
  from text_generation_server.pb import generate_pb2
  from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
label_desc: Use image node_id 4 for calling the CausalLM obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, position_ids, past_key_values) and returns: outputs,  outputs