code: def test_reproduce_fit(self):
      metric = Metric({"name": "logloss"})
      params = {
          "objective": "binary:logistic",
          "eval_metric": "logloss",
          "seed": 1,
          "ml_task": BINARY_CLASSIFICATION,
      }
      prev_loss = None
      for _ in range(3):
          xgb = XgbAlgorithm(params)
          xgb.fit(self.X, self.y)
          y_predicted = xgb.predict(self.X)
          loss = metric(self.y, y_predicted)
          if prev_loss is not None:
              assert_almost_equal(prev_loss, loss)
          prev_loss = loss
  
imports: import unittest
  import tempfile
  import json
  import numpy
  import pandas
  import os
  from numpy.testing import assert_almost_equal
  from sklearn import datasets
  from supervised.algorithms.xgboost import XgbAlgorithm, additional
  from supervised.utils.metric import Metric
  from supervised.utils.constants import BINARY_CLASSIFICATION
  import tempfile
label_desc: Use image node_id 2 for calling the XgboostAlgorithmTest obj's underlying member method code with example usage: obj.test_reproduce_fit() without return types