class_docstr: This loads an XGBoost model directly from a raw memory dump.
  
  We can't use the JSON dump because due to numerical precision issues those
  tree can actually be wrong when feature values land almost on a threshold.
class_docstr_tok: ["This","loads","an","XGBoost","model","directly","from","a","raw","memory","dump",".","We","ca","n't","use","the","JSON","dump","because","due","to","numerical","precision","issues","those","tree","can","actually","be","wrong","when","feature","values","land","almost","on","a","threshold","."]
code: def read_arr(self, dtype, n_items):
      format = "%d%s" % (n_items, dtype)
      size = struct.calcsize(format)
      val = struct.unpack(format, self.buf[self.pos : self.pos + size])[
          0
      ]
      self.pos += size
      return val
  
imports: import json
  import os
  import struct
  import time
  import warnings
  import numpy
  import pandas
  import scipy.sparse
  import scipy.special
  from packaging import version
  from ..None import maskers
  from .._explanation import Explanation
  from ..utils import assert_import, record_import_error, safe_isinstance
  from ..utils._exceptions import DimensionError, ExplainerError, InvalidFeaturePerturbationError, InvalidMaskerError, InvalidModelError
  from ..utils._legacy import DenseData
  from ._explainer import Explainer
label_desc: Use image node_id 4 for calling the XGBTreeModelLoader obj's underlying member method code with example usage: obj.read_arr(dtype, n_items) and returns: val