code: def test_sequence_passthrough_encoder(reduce_output: str):
      batch_size = 10
      sequence_length = 32
      sequence_passthrough_encoder = SequencePassthroughEncoder(
          reduce_output=reduce_output,
          max_sequence_length=sequence_length,
          encoding_size=8,
      ).to(DEVICE)
      inputs = torch.rand(batch_size, sequence_length, 8).to(DEVICE)
      outputs = sequence_passthrough_encoder(inputs)
      # SequencePassthroughEncoder does not implement output_shape, expect output to match input shape after reduce.
      assert (
          outputs[ENCODER_OUTPUT].shape[1:]
          == sequence_passthrough_encoder.reduce_sequence.output_shape
      )
  
file_path: ludwig/tests/ludwig/encoders/test_sequence_encoders.py
imports: from typing import Type
  import pytest
  import torch
  from ludwig.constants import ENCODER_OUTPUT
  from ludwig.encoders.sequence_encoders import ParallelCNN, SequenceEmbedEncoder, SequencePassthroughEncoder, StackedCNN, StackedCNNRNN, StackedParallelCNN, StackedRNN, StackedTransformer
  from ludwig.utils.torch_utils import get_torch_device
  from tests.integration_tests.parameter_update_utils import check_module_parameters_updated
label_desc: Use image node_id 1 for calling a global function with example usage: test_sequence_passthrough_encoder(reduce_output) without return types