class_docstr: Simulate distributed training.
class_docstr_tok: ["Simulate","distributed","training","."]
docstr: Compute the predictions using the model created in the fit step.
  
  predict_config is used to predict the training set train.txt
  The predictions are saved as predictions.txt and are then loaded to return them as a numpy array.
docstr_tok: ["Compute","the","predictions","using","the","model","created","in","the","fit","step",".","predict_config","is","used","to","predict","the","training","set","train.txt","The","predictions","are","saved","as","predictions.txt","and","are","then","loaded","to","return","them","as","a","numpy","array","."]
code: def predict(self, predict_config: Dict[str, Any]) -> np.ndarray:
      """Compute the predictions using the model created in the fit step.
  
      predict_config is used to predict the training set train.txt
      The predictions are saved as predictions.txt and are then loaded to return them as a numpy array.
      """
      self.predict_config = copy.deepcopy(self.default_predict_config)
      self.predict_config.update(predict_config)
      config_path = TESTS_DIR / "predict.conf"
      with open(config_path, "wt") as file:
          _write_dict(self.predict_config, file)
      cmd = [self.executable, f"config={config_path}"]
      result = subprocess.run(cmd)
      if result.returncode != 0:
          raise RuntimeError("Error in prediction")
      return np.loadtxt(str(TESTS_DIR / "predictions.txt"))
  
imports: import copy
  import io
  import socket
  import subprocess
  from concurrent.futures import ThreadPoolExecutor
  from pathlib import Path
  from typing import Any, Dict, Generator, List
  import numpy
  import pytest
  from sklearn.datasets import make_blobs, make_regression
  from sklearn.metrics import accuracy_score
label_desc: Use image node_id 6 for calling the DistributedMockup obj's underlying member method code with example usage: obj.predict(predict_config) and returns: np