code: def test_sequence_combiner_with_embed_encoder():
      config = {
          "combiner": {
              "encoder": {
                  "dropout": 0.1641014195584432,
                  "embedding_size": 256,
                  "type": "embed",
              },
              "main_sequence_feature": None,
              "type": "sequence",
          },
          "input_features": [
              {
                  "encoder": {"reduce_output": None, "type": "embed"},
                  "name": "Text",
                  "type": "text",
              }
          ],
          "model_type": "ecd",
          "output_features": [{"name": "Category", "type": "category"}],
          "preprocessing": {"sample_ratio": 0.05},
          "trainer": {"train_steps": 1},
      }
      with pytest.raises(ConfigValidationError):
          ModelConfig.from_dict(config)
  
imports: import contextlib
  from typing import Any, Dict, List, Optional
  import pytest
  import yaml
  from ludwig.constants import COMBINER, TYPE
  from ludwig.error import ConfigValidationError
  from ludwig.schema.model_types.base import ModelConfig
  from tests.integration_tests.utils import binary_feature, text_feature
label_desc: Use image node_id 2 for calling a global function with example usage: test_sequence_combiner_with_embed_encoder() without return types