code: def _prepare_data(csv_filename: str) -> Tuple[Dict, str]:
      input_features = [
          text_feature(name="title", reduce_output="sum"),
          text_feature(name="summary"),
          category_feature(vocab_size=3),
          category_feature(vocab_size=3),
      ]
  
      output_features = [
          text_feature(
              name="article", embedding_size=3, output_feature=True
          )
      ]
  
      dataset = generate_data(
          input_features, output_features, csv_filename
      )
  
      config = {
          INPUT_FEATURES: input_features,
          OUTPUT_FEATURES: output_features,
          COMBINER: {TYPE: "concat", "num_fc_layers": 2},
          TRAINER: {EPOCHS: 1, "learning_rate": 0.001, BATCH_SIZE: 128},
          DEFAULTS: {
              CATEGORY: {
                  PREPROCESSING: {
                      "missing_value_strategy": FILL_WITH_CONST,
                      "fill_value": "<CUSTOM_TOK>",
                  },
                  ENCODER: {TYPE: "sparse"},
                  DECODER: {
                      "norm_params": None,
                      "dropout": 0.1,
                      "use_bias": True,
                  },
              },
              TEXT: {
                  PREPROCESSING: {
                      "most_common": 10,
                      "padding_symbol": "<PADDING>",
                  },
                  ENCODER: {TYPE: "rnn"},
                  DECODER: {
                      TYPE: "generator",
                      "num_fc_layers": 2,
                      "dropout": 0.1,
                  },
                  LOSS: {"confidence_penalty": 0.1},
              },
          },
      }
  
      return config, dataset
  
file_path: ludwig/tests/integration_tests/test_config_global_defaults.py
imports: import logging
  from typing import Dict, Tuple
  from ludwig.constants import BATCH_SIZE, CATEGORY, COMBINER, DECODER, DEFAULTS, ENCODER, EPOCHS, FILL_WITH_CONST, INPUT_FEATURES, LOSS, OUTPUT_FEATURES, PREPROCESSING, TEXT, TRAINER, TYPE
  from ludwig.schema.model_config import ModelConfig
  from tests.integration_tests.utils import category_feature, generate_data, run_experiment, text_feature
label_desc: Use image node_id 1 for calling a global function with example usage: _prepare_data(csv_filename) and returns: config,  dataset