class_docstr: The interpolating polynomial for a set of points.
  
  Constructs a polynomial that passes through a given set of points.
  Allows evaluation of the polynomial, efficient changing of the y
  values to be interpolated, and updating by adding more x values.
  For reasons of numerical stability, this function does not compute
  the coefficients of the polynomial.
  The value `yi` need to be provided before the function is
  evaluated, but none of the preprocessing depends on them,
  so rapid updates are possible.
  
  Parameters
  ----------
  xi : cupy.ndarray
      1-D array of x-coordinates of the points the polynomial should
      pass through
  yi : cupy.ndarray, optional
      The y-coordinates of the points the polynomial should pass through.
      If None, the y values will be supplied later via the `set_y` method
  axis : int, optional
      Axis in the yi array corresponding to the x-coordinate values
  
  See Also
  --------
  scipy.interpolate.BarycentricInterpolator
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docstr: Evaluate the interpolating polynomial at the points x.
  
  Parameters
  ----------
  x : cupy.ndarray
      Points to evaluate the interpolant at
  
  Returns
  -------
  y : cupy.ndarray
      Interpolated values. Shape is determined by replacing the
      interpolation axis in the original array with the shape of x
  
  Notes
  -----
  Currently the code computes an outer product between x and the
  weights, that is, it constructs an intermediate array of size
  N by len(x), where N is the degree of the polynomial.
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code: def __call__(self, x):
      """Evaluate the interpolating polynomial at the points x.
  
      Parameters
      ----------
      x : cupy.ndarray
          Points to evaluate the interpolant at
  
      Returns
      -------
      y : cupy.ndarray
          Interpolated values. Shape is determined by replacing the
          interpolation axis in the original array with the shape of x
  
      Notes
      -----
      Currently the code computes an outer product between x and the
      weights, that is, it constructs an intermediate array of size
      N by len(x), where N is the degree of the polynomial.
  
      """
  
      return super().__call__(x)
  
imports: import cupy
  from cupyx.scipy._lib._util import _asarray_validated, float_factorial
label_desc: Use image node_id 4 for calling the BarycentricInterpolator obj's underlying member method code with example usage: obj.__call__(x) and returns: super