code: def __new__(
      cls, base_factor, target, correlation_length, mask=NotSpecified
  ):
      if target.ndim == 2 and base_factor.mask is not target.mask:
          raise IncompatibleTerms(term_1=base_factor, term_2=target)
  
      return super(_RollingCorrelation, cls).__new__(
          cls,
          inputs=[base_factor, target],
          window_length=correlation_length,
          mask=mask,
      )
  
imports: from numexpr import evaluate
  import numpy
  from numpy import broadcast_arrays
  from scipy.stats import linregress, spearmanr
  from zipline.assets import Asset
  from zipline.errors import IncompatibleTerms
  from zipline.pipeline.factors import CustomFactor
  from zipline.pipeline.filters import SingleAsset
  from zipline.pipeline.mixins import StandardOutputs
  from zipline.pipeline.sentinels import NotSpecified
  from zipline.pipeline.term import AssetExists
  from zipline.utils.input_validation import expect_bounded, expect_dtypes, expect_types
  from zipline.utils.math_utils import nanmean
  from zipline.utils.numpy_utils import float64_dtype, int64_dtype
  from .basic import Returns
label_desc: Use image node_id 1 for calling the _RollingCorrelation obj's underlying member method code with example usage: obj.__new__(cls, base_factor, target, correlation_length, mask) and returns: super