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 shape of the input arrays, excluding
      the interpolation axis
  dtype : Dtype of the y-data arrays. It can be set via _set_dtype,
      which forces it to be float or complex
  
  Methods
  -------
  __call__
  _prepare_x
  _finish_y
  _reshape_y
  _reshape_yi
  _set_yi
  _set_dtype
  _evaluate
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docstr: Evaluate the interpolant
  
  Parameters
  ----------
  x : cupy.ndarray
      The points to evaluate the interpolant
  
  Returns
  -------
  y : cupy.ndarray
      Interpolated values. Shape is determined by replacing
      the interpolation axis in the original array with the shape of x
  
  Notes
  -----
  Input values `x` must be convertible to `float` values like `int`
  or `float`.
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code: def __call__(self, x):
      """Evaluate the interpolant
  
      Parameters
      ----------
      x : cupy.ndarray
          The points to evaluate the interpolant
  
      Returns
      -------
      y : cupy.ndarray
          Interpolated values. Shape is determined by replacing
          the interpolation axis in the original array with the shape of x
  
      Notes
      -----
      Input values `x` must be convertible to `float` values like `int`
      or `float`.
  
      """
      x, x_shape = self._prepare_x(x)
      y = self._evaluate(x)
      return self._finish_y(y, x_shape)
  
imports: import cupy
  from cupyx.scipy._lib._util import _asarray_validated, float_factorial
label_desc: Use image node_id 2 for calling the _Interpolator1D obj's underlying member method code with example usage: obj.__call__(x) and returns: self