class_docstr: Common features in univariate interpolation
  
  Deal with input data type and interpolation axis rolling. The
  actual interpolator can assume the y-data is of shape (n, r) where
  `n` is the number of x-points, and `r` the number of variables,
  and use self.dtype as the y-data type.
  
  Attributes
  ----------
  _y_axis
      Axis along which the interpolation goes in the original array
  _y_extra_shape
      Additional trailing shape of the input arrays, excluding
      the interpolation axis.
  dtype
      Dtype of the y-data arrays. Can be set via _set_dtype, which
      forces it to be float or complex.
  
  Methods
  -------
  __call__
  _prepare_x
  _finish_y
  _reshape_yi
  _set_yi
  _set_dtype
  _evaluate
code: def _reshape_yi(self, yi, check=False):
      yi = np.rollaxis(np.asarray(yi), self._y_axis)
      if check and yi.shape[1:] != self._y_extra_shape:
          ok_shape = "%r + (N,) + %r" % (
              self._y_extra_shape[-self._y_axis :],
              self._y_extra_shape[: -self._y_axis],
          )
          raise ValueError("Data must be of shape %s" % ok_shape)
      return yi.reshape((yi.shape[0], -1))
  
imports: import numpy
  from scipy.special import factorial
  from scipy._lib._util import _asarray_validated, float_factorial
label_desc: Use image node_id 6 for calling the _Interpolator1D obj's underlying member method code with example usage: obj._reshape_yi(yi, check) and returns: yi