class_docstr: Feature group
  
  Parameters
  ----------
  group_hash : str
      The hash of the group
  features : List[np.ndarray]
      The features
  costs : List[float]
      The costs
  min_cost : float
      The minimum cost
class_docstr_tok: ["Feature","group","Parameters","--","--","--","--","--","group_hash",":","str","The","hash","of","the","group","features",":","List","[","np.ndarray","]","The","features","costs",":","List","[","float","]","The","costs","min_cost",":","float","The","minimum","cost"]
code: def append(
      self,
      features: List[np.ndarray],
      costs: np.ndarray,
  ) -> None:
      self.features.extend(features)
      self.costs = np.append(self.costs, costs)
      self.min_cost = np.min(self.costs)
  
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 2 for calling the FeatureGroup obj's underlying member method code with example usage: obj.append(features, costs) without return types