class_docstr: Base class for cubic regression spline stateful transforms
  
  This class contains all the functionality for the following stateful
  transforms:
   - ``cr(x, df=None, knots=None, lower_bound=None, upper_bound=None, constraints=None)``
     for natural cubic regression spline
   - ``cc(x, df=None, knots=None, lower_bound=None, upper_bound=None, constraints=None)``
     for cyclic cubic regression spline
class_docstr_tok: ["Base","class","for","cubic","regression","spline","stateful","transforms","This","class","contains","all","the","functionality","for","the","following","stateful","transforms",":","-","``","cr","(","x",",","df=None",",","knots=None",",","lower_bound=None",",","upper_bound=None",",","constraints=None",")","``","for","natural","cubic","regression","spline","-","``","cc","(","x",",","df=None",",","knots=None",",","lower_bound=None",",","upper_bound=None",",","constraints=None",")","``","for","cyclic","cubic","regression","spline"]
code: def memorize_chunk(
      self,
      x,
      df=None,
      knots=None,
      lower_bound=None,
      upper_bound=None,
      constraints=None,
  ):
      args = {
          "df": df,
          "knots": knots,
          "lower_bound": lower_bound,
          "upper_bound": upper_bound,
          "constraints": constraints,
      }
      self._tmp["args"] = args
  
      x = np.atleast_1d(x)
      if x.ndim == 2 and x.shape[1] == 1:
          x = x[:, 0]
      if x.ndim > 1:
          raise ValueError(
              "Input to %r must be 1-d, "
              "or a 2-d column vector." % (self._name,)
          )
  
      self._tmp.setdefault("xs", []).append(x)
  
imports: import numpy
  from patsy.util import have_pandas, atleast_2d_column_default, no_pickling, assert_no_pickling, safe_string_eq
  from patsy.state import stateful_transform
label_desc: Use image node_id 2 for calling the CubicRegressionSpline obj's underlying member method code with example usage: obj.memorize_chunk(x, df, knots, lower_bound, upper_bound, constraints) without return types