code: def run_boost_from_prediction_binary(
      tree_method, X, y, as_frame: Optional[Callable]
  ):
      """
      Parameters
      ----------
  
      as_frame: A callable function to convert margin into DataFrame, useful for different
      df implementations.
      """
  
      model_0 = xgb.XGBClassifier(
          learning_rate=0.3,
          random_state=0,
          n_estimators=4,
          tree_method=tree_method,
      )
      model_0.fit(X=X, y=y)
      margin = model_0.predict(X, output_margin=True)
      if as_frame is not None:
          margin = as_frame(margin)
  
      model_1 = xgb.XGBClassifier(
          learning_rate=0.3,
          random_state=0,
          n_estimators=4,
          tree_method=tree_method,
      )
      model_1.fit(X=X, y=y, base_margin=margin)
      predictions_1 = model_1.predict(X, base_margin=margin)
  
      cls_2 = xgb.XGBClassifier(
          learning_rate=0.3,
          random_state=0,
          n_estimators=8,
          tree_method=tree_method,
      )
      cls_2.fit(X=X, y=y)
      predictions_2 = cls_2.predict(X)
      np.testing.assert_allclose(predictions_1, predictions_2)
  
imports: import json
  import os
  import pickle
  import random
  import tempfile
  import warnings
  from typing import Callable, Optional
  import numpy
  import pytest
  from sklearn.utils.estimator_checks import parametrize_with_checks
  import xgboost
  from xgboost import testing
  from xgboost.testing.ranking import run_ranking_categorical, run_ranking_qid_df
  from xgboost.testing.shared import get_feature_weights, validate_data_initialization
  from xgboost.testing.updater import get_basescore
label_desc: Use image node_id 42 for calling a global function with example usage: run_boost_from_prediction_binary(tree_method, X, y, as_frame) without return types