class_docstr: Akima interpolator
  
  Fit piecewise cubic polynomials, given vectors x and y. The interpolation
  method by Akima uses a continuously differentiable sub-spline built from
  piecewise cubic polynomials. The resultant curve passes through the given
  data points and will appear smooth and natural.
  
  Parameters
  ----------
  x : ndarray, shape (m, )
      1-D array of monotonically increasing real values.
  y : ndarray, shape (m, ...)
      N-D array of real values. The length of `y` along the first axis must
      be equal to the length of `x`.
  axis : int, optional
      Specifies the axis of `y` along which to interpolate. Interpolation
      defaults to the first axis of `y`.
  
  Methods
  -------
  __call__
  derivative
  antiderivative
  roots
  
  See Also
  --------
  PchipInterpolator
  CubicSpline
  PPoly
  
  Notes
  -----
  .. versionadded:: 0.14
  
  Use only for precise data, as the fitted curve passes through the given
  points exactly. This routine is useful for plotting a pleasingly smooth
  curve through a few given points for purposes of plotting.
  
  References
  ----------
  [1] A new method of interpolation and smooth curve fitting based
      on local procedures. Hiroshi Akima, J. ACM, October 1970, 17(4),
      589-602.
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code: def from_bernstein_basis(cls, bp, extrapolate=None):
      raise NotImplementedError(
          "This method does not make sense for "
          "an Akima interpolator."
      )
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from scipy._lib.six import string_types
  from .None import BPoly, PPoly
  from .polyint import _isscalar
  from scipy._lib._util import _asarray_validated
  from scipy.linalg import solve_banded, solve
label_desc: Use image node_id 4 for calling the Akima1DInterpolator obj's underlying member method code with example usage: obj.from_bernstein_basis(cls, bp, extrapolate) without return types