code: def _get_inputs(
      self, n_mel=80, n_batch=16, max_mel_specgram_length=300
  ):
      mel_specgram = torch.rand(
          n_batch,
          n_mel,
          max_mel_specgram_length,
          dtype=self.dtype,
          device=self.device,
      )
      mel_specgram_postnet = torch.rand(
          n_batch,
          n_mel,
          max_mel_specgram_length,
          dtype=self.dtype,
          device=self.device,
      )
      gate_out = torch.rand(
          n_batch, dtype=self.dtype, device=self.device
      )
      truth_mel_specgram = torch.rand(
          n_batch,
          n_mel,
          max_mel_specgram_length,
          dtype=self.dtype,
          device=self.device,
      )
      truth_gate_out = torch.rand(
          n_batch, dtype=self.dtype, device=self.device
      )
  
      truth_mel_specgram.requires_grad = False
      truth_gate_out.requires_grad = False
  
      return (
          mel_specgram,
          mel_specgram_postnet,
          gate_out,
          truth_mel_specgram,
          truth_gate_out,
      )
  
file_path: audio/test/torchaudio_unittest/example/tacotron2/tacotron2_loss_impl.py
imports: import torch
  from pipeline_tacotron2.loss import Tacotron2Loss
  from torch.autograd import gradcheck, gradgradcheck
  from torchaudio_unittest.common_utils import TestBaseMixin, torch_script
label_desc: Use image node_id 1 for calling the Tacotron2LossInputMixin obj's underlying member method code with example usage: obj._get_inputs(n_mel, n_batch, max_mel_specgram_length) and returns: mel_specgram,  mel_specgram_postnet,  gate_out,  truth_mel_specgram,  truth_gate_out