code: def _get_inputs(
      self,
      n_mels: int,
      n_batch: int,
      max_mel_specgram_length: int,
      max_text_length: int,
  ):
      text = torch.randint(
          0,
          148,
          (n_batch, max_text_length),
          dtype=torch.int32,
          device=self.device,
      )
      text_lengths = max_text_length * torch.ones(
          (n_batch,), dtype=torch.int32, device=self.device
      )
      mel_specgram = torch.rand(
          n_batch,
          n_mels,
          max_mel_specgram_length,
          dtype=self.dtype,
          device=self.device,
      )
      mel_specgram_lengths = max_mel_specgram_length * torch.ones(
          (n_batch,), dtype=torch.int32, device=self.device
      )
      return text, text_lengths, mel_specgram, mel_specgram_lengths
  
imports: from typing import Tuple
  import torch
  from parameterized import parameterized
  from torch import Tensor
  from torchaudio.models import Tacotron2
  from torchaudio.models.tacotron2 import _Decoder, _Encoder
  from torchaudio_unittest.common_utils import skipIfPy310, TestBaseMixin, torch_script
label_desc: Use image node_id 1 for calling the Tacotron2Tests obj's underlying member method code with example usage: obj._get_inputs(n_mels, n_batch, max_mel_specgram_length, max_text_length) and returns: text,  text_lengths,  mel_specgram,  mel_specgram_lengths