code: def _train(input_features, output_features, data_csv, **kwargs):
      config = {
          "input_features": input_features,
          "output_features": output_features,
          "combiner": {"type": "concat", "output_size": 14},
          TRAINER: {"epochs": 2, BATCH_SIZE: 128},
      }
  
      model = LudwigModel(config)
      _, _, output_dir = model.train(dataset=data_csv, **kwargs)
      return model, output_dir
  
file_path: ludwig/tests/integration_tests/test_collect.py
imports: import os
  import shutil
  import numpy
  import torch
  from ludwig.api import LudwigModel
  from ludwig.collect import collect_activations, collect_weights, print_model_summary
  from ludwig.constants import BATCH_SIZE, ENCODER, TRAINER, TYPE
  from ludwig.utils.torch_utils import get_torch_device
  from tests.integration_tests.utils import category_feature, ENCODERS, generate_data, sequence_feature
label_desc: Use image node_id 2 for calling a global function with example usage: _train(input_features, output_features, data_csv) and returns: model,  output_dir