code: def _create_inputs(batch_size=8):
      max_len_t, max_len_m = random.randint(25, 50), random.randint(
          50, 80
      )
      input_dummy = (
          torch.randint(0, 24, (batch_size, max_len_t))
          .long()
          .to(device)
      )
      input_lengths = (
          torch.randint(20, max_len_t, (batch_size,))
          .long()
          .to(device)
          .sort(descending=True)[0]
      )
      input_lengths[0] = max_len_t
      input_dummy = input_dummy * sequence_mask(input_lengths)
      mel_spec = torch.randn(
          batch_size, max_len_m, config_global.audio["num_mels"]
      ).to(device)
      mel_lengths = (
          torch.randint(40, max_len_m, (batch_size,))
          .long()
          .to(device)
          .sort(descending=True)[0]
      )
      mel_lengths[0] = max_len_m
      mel_spec = mel_spec * sequence_mask(mel_lengths).unsqueeze(2)
      return input_dummy, input_lengths, mel_spec, mel_lengths
  
imports: import os
  import random
  import unittest
  from copy import deepcopy
  import torch
  from tests import get_tests_output_path
  from TTS.tts.configs.overflow_config import OverflowConfig
  from TTS.tts.layers.overflow.common_layers import Encoder, Outputnet, OverflowUtils
  from TTS.tts.layers.overflow.decoder import Decoder
  from TTS.tts.layers.overflow.neural_hmm import EmissionModel, NeuralHMM, TransitionModel
  from TTS.tts.models.overflow import Overflow
  from TTS.tts.utils.helpers import sequence_mask
  from TTS.utils.audio import AudioProcessor
label_desc: Use image node_id 1 for calling a global function with example usage: _create_inputs(batch_size) and returns: input_dummy,  input_lengths,  mel_spec,  mel_lengths