code: def test_config_object_defaults():
      config = {
          "input_features": [
              {"name": "number_feature", "type": "number"},
              {
                  "name": "text_feature_1",
                  "type": "text",
                  "encoder": {
                      "type": "rnn",
                      "activation": "sigmoid",
                  },
              },
              {
                  "name": "text_feature_2",
                  "type": "text",
              },
          ],
          "output_features": [
              {
                  "name": "number_output_feature",
                  "type": "number",
              },
          ],
          "defaults": {
              "number": {
                  "preprocessing": {
                      "missing_value_strategy": "drop_row"
                  },
                  "encoder": {"type": "dense"},
              },
              "text": {
                  "preprocessing": {
                      "missing_value_strategy": "drop_row",
                  },
                  "encoder": {
                      "type": "stacked_parallel_cnn",
                      "activation": "tanh",
                  },
              },
          },
      }
  
      config_object = ModelConfig.from_dict(config)
      assert (
          config_object.input_features.number_feature.preprocessing.missing_value_strategy
          == "drop_row"
      )
      assert (
          config_object.input_features.number_feature.encoder.type
          == "dense"
      )
  
      assert (
          config_object.input_features.text_feature_1.encoder.type
          == "rnn"
      )
      assert (
          config_object.input_features.text_feature_1.encoder.activation
          == "sigmoid"
      )
      assert (
          config_object.input_features.text_feature_1.preprocessing.missing_value_strategy
          == "drop_row"
      )
  
      assert (
          config_object.input_features.text_feature_2.encoder.type
          == "stacked_parallel_cnn"
      )
      assert (
          config_object.input_features.text_feature_2.encoder.activation
          == "tanh"
      )
      assert (
          config_object.input_features.text_feature_2.preprocessing.missing_value_strategy
          == "drop_row"
      )
  
imports: import os
  from tempfile import TemporaryDirectory
  from typing import Any, Dict, Optional, Union
  import pytest
  import yaml
  from ludwig.constants import ACTIVE, BASE_MODEL, CLIP, COLUMN, COMBINER, DECODER, DEFAULT_VALIDATION_METRIC, DEFAULTS, DEPENDENCIES, ENCODER, HYPEROPT, INPUT_FEATURES, INPUT_SIZE, LOSS, MODEL_ECD, MODEL_GBM, MODEL_LLM, MODEL_TYPE, NAME, NUM_CLASSES, OPTIMIZER, OUTPUT_FEATURES, PREPROCESSING, PROC_COLUMN, REDUCE_DEPENDENCIES, REDUCE_INPUT, TIED, TRAINER, TYPE
  from ludwig.error import ConfigValidationError
  from ludwig.schema.decoders.base import ClassifierConfig
  from ludwig.schema.encoders.text_encoders import BERTConfig
  from ludwig.schema.features.augmentation.image import RandomBlurConfig, RandomRotateConfig
  from ludwig.schema.features.image_feature import AUGMENTATION_DEFAULT_OPERATIONS
  from ludwig.schema.features.number_feature import NumberOutputFeatureConfig
  from ludwig.schema.features.text_feature import TextOutputFeatureConfig
  from ludwig.schema.llms.quantization import QuantizationConfig
  from ludwig.schema.model_config import ModelConfig
  from ludwig.schema.utils import BaseMarshmallowConfig, convert_submodules
label_desc: Use image node_id 2 for calling a global function with example usage: test_config_object_defaults() without return types