class_docstr: A Term that should be computed from a tuple of inputs.
  
  This is the base class for :class:`zipline.pipeline.Factor`,
  :class:`zipline.pipeline.Filter`, and :class:`zipline.pipeline.Classifier`.
class_docstr_tok: ["A","Term","that","should","be","computed","from","a","tuple","of","inputs",".","This","is","the","base","class","for",":","class",":","`","zipline.pipeline.Factor","`",",",":","class",":","`","zipline.pipeline.Filter","`",",","and",":","class",":","`","zipline.pipeline.Classifier","`","."]
docstr: Called with a column of the result of a pipeline. This needs to put
  the data into a format that can be used in a workspace to continue
  doing computations.
  
  Parameters
  ----------
  result : pd.Series
      A multiindexed series with (dates, assets) whose values are the
      results of running this pipeline term over the dates.
  assets : pd.Index
      All of the assets being requested. This allows us to correctly
      shape the workspace value.
  
  Returns
  -------
  workspace_value : array-like
      An array like value that the engine can consume.
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code: def to_workspace_value(self, result, assets):
      """
      Called with a column of the result of a pipeline. This needs to put
      the data into a format that can be used in a workspace to continue
      doing computations.
  
      Parameters
      ----------
      result : pd.Series
          A multiindexed series with (dates, assets) whose values are the
          results of running this pipeline term over the dates.
      assets : pd.Index
          All of the assets being requested. This allows us to correctly
          shape the workspace value.
  
      Returns
      -------
      workspace_value : array-like
          An array like value that the engine can consume.
      """
      return (
          result.unstack()
          .fillna(self.missing_value)
          .reindex(
              columns=assets,
              fill_value=self.missing_value,
          )
          .values
      )
  
imports: from abc import ABCMeta, abstractproperty, abstractmethod
  from bisect import insort
  from collections import Mapping
  from weakref import WeakValueDictionary
  from numpy import array, dtype, ndarray
  from six import with_metaclass
  from zipline.assets import Asset
  from zipline.errors import DTypeNotSpecified, InvalidOutputName, NonSliceableTerm, NonWindowSafeInput, NotDType, NonPipelineInputs, TermInputsNotSpecified, TermOutputsEmpty, UnsupportedDType, WindowLengthNotSpecified
  from zipline.lib.adjusted_array import can_represent_dtype
  from zipline.lib.labelarray import LabelArray
  from zipline.utils.input_validation import expect_types
  from zipline.utils.memoize import classlazyval, lazyval
  from zipline.utils.numpy_utils import bool_dtype, categorical_dtype, datetime64ns_dtype, default_missing_value_for_dtype, float64_dtype
  from zipline.utils.sharedoc import templated_docstring, PIPELINE_ALIAS_NAME_DOC, PIPELINE_DOWNSAMPLING_FREQUENCY_DOC
  from .domain import Domain, GENERIC, infer_domain
  from .downsample_helpers import expect_downsample_frequency
  from .sentinels import NotSpecified
label_desc: Use image node_id 10 for calling the ComputableTerm obj's underlying member method code with example usage: obj.to_workspace_value(result, assets) and returns: result