class_docstr: Decorator that marks that an attribute of an instance should not be
  computed until needed, and that the value should be memoized.
  
  Lifted from quantopian/trading_calendars
class_docstr_tok: ["Decorator","that","marks","that","an","attribute","of","an","instance","should","not","be","computed","until","needed",",","and","that","the","value","should","be","memoized",".","Lifted","from","quantopian\/trading_calendars"]
code: def __get__(self, instance, owner):
      if instance is None:
          return self
      try:
          return self._cache[instance]
      except KeyError:
          self._cache[instance] = val = self._get(instance)
          return val
  
imports: import math
  import numpy
  import random
  import scipy.sparse
  import six
  import tensorflow
  from weakref import WeakKeyDictionary
  from .input_utils import create_tensorrec_dataset_from_sparse_matrix, create_tensorrec_dataset_from_tfrecord
label_desc: Use image node_id 2 for calling the lazyval obj's underlying member method code with example usage: obj.__get__(instance, owner) and returns: self, self, val