code: def forward(
      self,
      input_ids: torch.LongTensor = None,
      past_key_values: Optional[List[torch.FloatTensor]] = None,
      *args,
      **kwargs,
  ):
      # Reinit model and clean memory
      del self.model
      gc.collect()
      torch.xpu.empty_cache()
      self._init_model()
  
      # Send batch to device
      inputs = input_ids.to(self.device)
  
      # Set up kv cache
      kv_cache = {}
      if past_key_values is None:
          past_key_values = {}
  
      with torch.inference_mode():
          # Generate attention mask and position ids
          current_shape = inputs.shape[1]
          if past_key_values.get(self.layer_names[1], None):
              pre_shape = past_key_values[self.layer_names[1]][0].size(
                  2
              )
          else:
              pre_shape = 0
          pos = self.position_ids[
              :, pre_shape : current_shape + pre_shape
          ]
          attn = self.attention_mask[
              :, :, -current_shape:, -current_shape - pre_shape :
          ]
  
          for layer_name, layer in tqdm(
              zip(self.layer_names, self.layers), total=len(self.layers)
          ):
              # Load current layer to device
              state_dict = load_file(
                  os.path.join(
                      self.splitted_weights_path,
                      f"{layer_name}.safetensors",
                  ),
                  device="cpu",
              )
              self.move_layer_to_device(state_dict)
  
              # Run layer
              if layer_name in (
                  "model.embed_tokens",
                  "model.norm",
                  "lm_head",
              ):
                  inputs = layer(inputs)
              else:
                  inputs, new_kv_cache = layer(
                      inputs,
                      use_cache=True,
                      past_key_value=past_key_values.get(
                          layer_name, None
                      ),
                      position_ids=pos,
                      attention_mask=attn,
                  )
                  kv_cache[layer_name] = new_kv_cache
  
              # Delete weight before moving to('meta')
              for module in layer.modules():
                  if hasattr(module, "weight"):
                      del module.weight
  
              # Remove previous layer from memory (including buffers)
              layer.to("meta")
  
      result = CausalLMOutputWithPast(
          logits=inputs.detach(),
          past_key_values=kv_cache,
      )
      return result
  
imports: import argparse
  import gc
  import os
  import time
  import warnings
  from typing import Dict, List, Optional, Union
  import torch
  import torch.nn
  from accelerate import init_empty_weights
  from safetensors.torch import load_file, save_file
  from tqdm.auto import tqdm
  from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
  from transformers.generation import GenerationMixin
  from transformers.modeling_outputs import CausalLMOutputWithPast
  from bigdl.llm import optimize_model
  from bigdl.llm.transformers.low_bit_linear import FP4Params, LowBitLinear
label_desc: Use image node_id 9 for calling the LowMemoryLlama obj's underlying member method code with example usage: obj.forward(input_ids, past_key_values) and returns: result