code: def test_binary_encoders(csv_filename):
      config = {
          "input_features": [
              {
                  "name": "binary1",
                  "type": BINARY,
                  "encoder": {"type": "passthrough"},
              },
              {
                  "name": "binary2",
                  "type": BINARY,
                  "encoder": {"type": "dense"},
              },
          ],
          "output_features": [binary_feature(output_feature=True)],
          TRAINER: {"train_steps": 1},
      }
      generate_data_and_train(config, csv_filename)
  
imports: from contextlib import nullcontext
  from ludwig.api import LudwigModel
  from ludwig.constants import BINARY, TRAINER
  from tests.integration_tests.utils import binary_feature, category_feature, generate_data
label_desc: Use image node_id 3 for calling a global function with example usage: test_binary_encoders(csv_filename) without return types