class_docstr: A Filter indicating whether input values are missing from an input.
  
  Parameters
  ----------
  factor : catalyst.pipeline.Term
      The factor to compare against its missing_value.
class_docstr_tok: ["A","Filter","indicating","whether","input","values","are","missing","from","an","input",".","Parameters","--","--","--","--","--","factor",":","catalyst.pipeline.Term","The","factor","to","compare","against","its","missing_value","."]
code: def __new__(cls, term):
      return super(NullFilter, cls).__new__(
          cls,
          inputs=(term,),
      )
  
imports: from itertools import chain
  from operator import attrgetter
  from numpy import float64, nan, nanpercentile
  from catalyst.errors import BadPercentileBounds, NonExistentAssetInTimeFrame, UnsupportedDataType
  from catalyst.lib.labelarray import LabelArray
  from catalyst.lib.rank import is_missing
  from catalyst.pipeline.expression import BadBinaryOperator, FILTER_BINOPS, method_name_for_op, NumericalExpression
  from catalyst.pipeline.mixins import AliasedMixin, CustomTermMixin, DownsampledMixin, LatestMixin, PositiveWindowLengthMixin, RestrictedDTypeMixin, SingleInputMixin
  from catalyst.pipeline.term import ComputableTerm, Term
  from catalyst.utils.input_validation import expect_types
  from catalyst.utils.memoize import classlazyval
  from catalyst.utils.numpy_utils import bool_dtype, repeat_first_axis
label_desc: Use image node_id 1 for calling the NullFilter obj's underlying member method code with example usage: obj.__new__(cls, term) and returns: super