code: def megatron_gpt_generate(
      model,
      inputs,
      tokenizer,
      length_params,
      sampling_params,
      **strategy_args,
  ):
      # reproduce the old compute_prob method
      # a very special case
      if sampling_params["compute_logprob"]:
          # need to overwrite some configuration, make it immutable
          sampling_params = sampling_params.copy()
          length_params = length_params.copy()
          length_params["max_length"] = 1
          sampling_params["all_probs"] = True
          sampling_params["add_BOS"] = False
          sampling_params["greedy"] = True
          response = generate(
              model,
              inputs=inputs,
              tokens_to_generate=length_params["max_length"],
              all_probs=sampling_params["all_probs"],
              compute_logprob=sampling_params["compute_logprob"],
              temperature=sampling_params["temperature"],
              add_BOS=sampling_params["add_BOS"],
              top_k=sampling_params["top_k"],
              top_p=sampling_params["top_p"],
              greedy=sampling_params["use_greedy"],
              repetition_penalty=sampling_params["repetition_penalty"],
              end_strings=sampling_params["end_strings"],
              min_tokens_to_generate=length_params["min_length"],
              compute_attention_mask=sampling_params.get(
                  "compute_attention_mask", True
              ),
              **strategy_args,
          )
          compute_prob_response = get_computeprob_response(
              tokenizer, response, inputs
          )
          return compute_prob_response
  
      if isinstance(inputs, (list, tuple)):
          if isinstance(inputs[0], (str, torch.Tensor)):
              output = generate(
                  model,
                  inputs=inputs,
                  tokens_to_generate=length_params["max_length"],
                  all_probs=sampling_params["all_probs"],
                  compute_logprob=sampling_params["compute_logprob"],
                  temperature=sampling_params["temperature"],
                  add_BOS=sampling_params["add_BOS"],
                  top_k=sampling_params["top_k"],
                  top_p=sampling_params["top_p"],
                  greedy=sampling_params["use_greedy"],
                  repetition_penalty=sampling_params[
                      "repetition_penalty"
                  ],
                  end_strings=sampling_params["end_strings"],
                  min_tokens_to_generate=length_params["min_length"],
                  **strategy_args,
              )
              return output
          elif isinstance(inputs[0], dict):
              raise NotImplementedError("json object not implemented")
          else:
              raise NotImplementedError(
                  "unknown type is not implemented"
              )
      else:
          raise NotImplementedError("unknown type is not implemented")
  
imports: import os
  import pickle
  import re
  from collections.abc import Iterable
  from functools import partial
  from typing import Callable, Tuple
  import numpy
  import torch
  import torch.nn.functional
  from nemo.collections.common.tokenizers.tabular_tokenizer import TabularTokenizer
  from nemo.collections.nlp.modules.common.megatron.utils import get_ltor_masks_and_position_ids
  from nemo.collections.nlp.modules.common.text_generation_strategy import model_inference_strategy_dispatcher
  from nemo.collections.nlp.modules.common.transformer.text_generation import LengthParam, OutputType, SamplingParam
  from nemo.utils import AppState
label_desc: Use image node_id 3 for calling a global function with example usage: megatron_gpt_generate(model, inputs, tokenizer, length_params, sampling_params) and returns: compute_prob_response, output