code: def generate_past_key_values(model, batch_size, seq_len):
      (
          num_block_layers,
          num_attention_heads,
          num_embedding_size_per_head,
      ) = sparse_model_config(model.config)
      if model.config.model_type == "bloom":
          past_key_values = tuple(
              (
                  torch.empty(
                      int(num_attention_heads * batch_size),
                      num_embedding_size_per_head,
                      seq_len,
                  )
                  .to(model.dtype)
                  .to(model.device),
                  torch.empty(
                      int(num_attention_heads * batch_size),
                      seq_len,
                      num_embedding_size_per_head,
                  )
                  .to(model.dtype)
                  .to(model.device),
              )
              for _ in range(num_block_layers)
          )
      else:
          past_key_values = tuple(
              (
                  torch.empty(
                      batch_size,
                      num_attention_heads,
                      seq_len,
                      num_embedding_size_per_head,
                  )
                  .to(model.dtype)
                  .to(model.device),
                  torch.empty(
                      batch_size,
                      num_attention_heads,
                      seq_len,
                      num_embedding_size_per_head,
                  )
                  .to(model.dtype)
                  .to(model.device),
              )
              for _ in range(num_block_layers)
          )
      return past_key_values
  
imports: import argparse
  import inspect
  import logging
  from typing import Tuple
  import torch
  from accelerate import PartialState
  from accelerate.utils import set_seed
  from transformers import AutoTokenizer, BloomForCausalLM, BloomTokenizerFast, CTRLLMHeadModel, CTRLTokenizer, GenerationMixin, GPT2LMHeadModel, GPT2Tokenizer, GPTJForCausalLM, LlamaForCausalLM, LlamaTokenizer, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, OPTForCausalLM, TransfoXLLMHeadModel, TransfoXLTokenizer, XLMTokenizer, XLMWithLMHeadModel, XLNetLMHeadModel, XLNetTokenizer
  from transformers.modeling_outputs import CausalLMOutputWithPast
label_desc: Use image node_id 7 for calling a global function with example usage: generate_past_key_values(model, batch_size, seq_len) and returns: past_key_values