class_docstr: XGBoost model
  
  Parameters
  ----------
  extractor : FeatureExtractor
      The feature extractor for the model.
  config : XGBConfig
      The XGBoost model config.
  num_warmup_samples : int
      The number of samples that are used for warmup, i.e., the first few samples are predicted
      with random results.
  early_stopping_rounds : int
      The number of rounds for early stopping.
  verbose_eval : int
      The verbose level when doing evaluation.
  average_peak_n : int
      The number to calculate average peak score.
  adaptive_training : bool
      Whether use adaptive training to reduce tuning time.
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code: def _predict(  # type: ignore # pylint: disable=invalid-name
      self,
      xs: List[np.ndarray],
  ) -> np.ndarray:
      d_test = PackSum(xs=xs, ys=None)
      pred = self.booster.predict(d_test.dmatrix)
      ret = d_test.predict_with_score(pred)
      return ret
  
imports: import os
  import tempfile
  from collections import OrderedDict
  from itertools import chain
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, NamedTuple, Optional, Tuple
  from typing_extensions import Literal
  import numpy
  from ...contrib.tar import tar, untar
  from ...runtime import NDArray
  from ..cost_model import PyCostModel
  from ..feature_extractor import FeatureExtractor
  from ..logging import get_logger
  from ..runner import RunnerResult
  from ..search_strategy import MeasureCandidate
  from ..utils import cpu_count, derived_object, shash2hex
  from .metric import max_curve
label_desc: Use image node_id 7 for calling the XGBModel obj's underlying member method code with example usage: obj._predict(xs) and returns: ret