code: def _prepare_data(csv_filename):
      # Single sequence input, single category output
      input_features = [
          sequence_feature(encoder={"reduce_output": "sum"})
      ]
      output_features = [
          category_feature(
              decoder={"vocab_size": 2}, reduce_input="sum"
          )
      ]
  
      input_features[0][ENCODER][TYPE] = ENCODERS[0]
  
      # Generate test data
      data_csv = generate_data(
          input_features, output_features, csv_filename
      )
      return input_features, output_features, data_csv
  
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 1 for calling a global function with example usage: _prepare_data(csv_filename) and returns: input_features,  output_features,  data_csv