docstr: Feed tensors with specific shape to Tacotron2 and validate
  that it outputs with a tensor with expected shape.
code: def test_tacotron2_output_shape(self, n_batch):
      r"""Feed tensors with specific shape to Tacotron2 and validate
      that it outputs with a tensor with expected shape.
      """
      n_mels = 80
      max_mel_specgram_length = 300
      max_text_length = 100
  
      model = _get_tacotron2_model(n_mels).to(self.device).eval()
      inputs = self._get_inputs(
          n_mels, n_batch, max_mel_specgram_length, max_text_length
      )
      mel_out, mel_out_postnet, gate_outputs, alignments = model(
          *inputs
      )
  
      assert mel_out.size() == (
          n_batch,
          n_mels,
          max_mel_specgram_length,
      )
      assert mel_out_postnet.size() == (
          n_batch,
          n_mels,
          max_mel_specgram_length,
      )
      assert gate_outputs.size() == (n_batch, max_mel_specgram_length)
      assert alignments.size() == (
          n_batch,
          max_mel_specgram_length,
          max_text_length,
      )
  
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 3 for calling the Tacotron2Tests obj's underlying member method code with example usage: obj.test_tacotron2_output_shape(n_batch) without return types