docstr: Feed tensors with specific shape to Tacotron2 Decoder and validate
  that it outputs with a tensor with expected shape.
code: def test_encoder_output_shape(self):
      r"""Feed tensors with specific shape to Tacotron2 Decoder and validate
      that it outputs with a tensor with expected shape.
      """
      n_batch, n_seq, encoder_embedding_dim = 16, 64, 512
      model = (
          _Encoder(
              encoder_embedding_dim=encoder_embedding_dim,
              encoder_n_convolution=3,
              encoder_kernel_size=5,
          )
          .to(self.device)
          .eval()
      )
  
      x = torch.rand(
          n_batch,
          encoder_embedding_dim,
          n_seq,
          device=self.device,
          dtype=self.dtype,
      )
      input_lengths = (
          torch.ones(n_batch, device=self.device, dtype=torch.int32)
          * n_seq
      )
      out = model(x, input_lengths)
  
      assert out.size() == (n_batch, n_seq, encoder_embedding_dim)
  
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 2 for calling the Tacotron2EncoderTests obj's underlying member method code with example usage: obj.test_encoder_output_shape() without return types