class_docstr: Mixin for behavior shared by Downsampled{Factor,Filter,Classifier}
  
  A downsampled term is a wrapper around the "real" term that performs actual
  computation. The downsampler is responsible for calling the real term's
  `compute` method at selected intervals and forward-filling the computed
  values.
  
  Downsampling is not currently supported for terms with multiple outputs.
code: def _init(self, frequency, wrapped_term, *args, **kwargs):
      self._frequency = frequency
      self._wrapped_term = wrapped_term
      return super(DownsampledMixin, self)._init(*args, **kwargs)
  
imports: from textwrap import dedent
  from numpy import array, full, recarray, vstack
  from pandas import NaT
  from catalyst.errors import WindowLengthNotPositive, UnsupportedDataType, NoFurtherDataError
  from catalyst.utils.control_flow import nullctx
  from catalyst.utils.input_validation import expect_types
  from catalyst.utils.sharedoc import format_docstring, PIPELINE_ALIAS_NAME_DOC, PIPELINE_DOWNSAMPLING_FREQUENCY_DOC
  from catalyst.utils.pandas_utils import nearest_unequal_elements
  from .downsample_helpers import select_sampling_indices, expect_downsample_frequency
  from .sentinels import NotSpecified
  from .term import Term
label_desc: Use image node_id 2 for calling the DownsampledMixin obj's underlying member method code with example usage: obj._init(frequency, wrapped_term) and returns: super