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
docstr: Evaluate the interpolant
  
  Parameters
  ----------
  x : array_like
      Points to evaluate the interpolant at.
  
  Returns
  -------
  y : array_like
      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`.
docstr_tok: ["Evaluate","the","interpolant","Parameters","--","--","--","--","--","x",":","array_like","Points","to","evaluate","the","interpolant","at",".","Returns","--","--","--","-","y",":","array_like","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","`","."]
code: def __call__(self, x):
      """
      Evaluate the interpolant
  
      Parameters
      ----------
      x : array_like
          Points to evaluate the interpolant at.
  
      Returns
      -------
      y : array_like
          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 numpy
  from scipy.special import factorial
  from 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