docstr: Validate the torchscript consistency of a Decoder.
code: def test_decoder_torchscript_consistency(self, n_batch):
      r"""Validate the torchscript consistency of a Decoder."""
      n_mels = 80
      n_seq = 200
      encoder_embedding_dim = 256
      n_time_steps = 150
  
      model = _get_decoder_model(
          n_mels=n_mels, encoder_embedding_dim=encoder_embedding_dim
      )
      model = model.to(self.device).eval()
  
      memory = torch.rand(
          n_batch,
          n_seq,
          encoder_embedding_dim,
          dtype=self.dtype,
          device=self.device,
      )
      decoder_inputs = torch.rand(
          n_batch,
          n_mels,
          n_time_steps,
          dtype=self.dtype,
          device=self.device,
      )
      memory_lengths = torch.ones(
          n_batch, dtype=torch.int32, device=self.device
      )
  
      self._assert_torchscript_consistency(
          model, (memory, decoder_inputs, memory_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 Tacotron2DecoderTests obj's underlying member method code with example usage: obj.test_decoder_torchscript_consistency(n_batch) without return types