text: """
  The seeker module containing the `seeker(...)` function.
  """
  # pylint: disable=fixme
  from typing import Dict
  import numpy as np
  from utils.data_preprocess import preprocess_data
  
  
  def seeker(input_dict: Dict) -> np.ndarray:
      """Solution seeker function.
  
      Args:
          input_dict (Dict): Dictionary that contains the seeker function inputs, as below:
              * "seed" (int): Random seed provided by the competition, use for reproducibility.
              * "generated_data" (np.ndarray of float): Generated dataset from hider data, 
                  shape [num_examples, max_seq_len, num_features].
              * "enlarged_data" (np.ndarray of float): Enlarged original dataset, 
                  shape [num_examples_enlarge, max_seq_len, num_features].
              * "generated_data_padding_mask" (np.ndarray of bool): Padding mask of bools, generated dataset, 
                  same shape as "generated_data".
              * "enlarged_data_padding_mask" (np.ndarray of bool): Padding mask of bools, enlarged dataset, 
                  same shape as "enlarged_data".
  
      Returns:
          np.ndarray: The reidentification labels produced by the seeker, expected shape [num_examples_enlarge].
      """
  
      # Get the inputs.
      seed = input_dict["seed"]
      generated_data = input_dict["generated_data"]
      enlarged_data = input_dict["enlarged_data"]
      generated_data_padding_mask = input_dict["generated_data_padding_mask"]
      enlarged_data_padding_mask = input_dict["enlarged_data_padding_mask"]
  
      # Get processed and imputed data, if desired:
      generated_data_preproc, generated_data_imputed = preprocess_data(generated_data, generated_data_padding_mask)
      enlarged_data_preproc, enlarged_data_imputed = preprocess_data(enlarged_data, enlarged_data_padding_mask)
  
      # TODO: Put your seeker code to replace Example 1 below.
      # Feel free play around with Examples 1 (knn) and 2 (binary_predictor) below.
  
      # --- Example 1: knn ---
      from examples.seeker.knn import knn_seeker
  
      reidentified_data = knn_seeker.knn_seeker(generated_data_imputed, enlarged_data_imputed)
      return reidentified_data
  
      # --- Example 2: binary_predictor ---
      # from utils.misc import tf115_found
      # assert tf115_found is True, "TensorFlow 1.15 not found, which is required to run binary_predictor."
      # from examples.seeker.binary_predictor import binary_predictor
      # reidentified_data = binary_predictor.binary_predictor(generated_data_imputed, enlarged_data_imputed, verbose=True)
      # return generated_data
  