s that interpolates `func` at the Chebyshev
points of the first kind in the interval [-1, 1]. The interpolating
series tends to a minmax approximation to `func` with increasing `deg`
if the function is continuous in the interval.

Parameters
----------
func : function
    The function to be approximated. It must be a function of a single
    variable of the form ``f(x, a, b, c...)``, where ``a, b, c...`` are
    extra arguments passed in the `args` parameter.
deg : int
    Degree of the interpolating polynomial
args : tuple, optional
    Extra arguments to be used in the function call. Default is no extra
    arguments.

Returns
-------
coef : ndarray, shape (deg + 1,)
    Chebyshev coefficients of the interpolating series ordered from low to
    high.

Examples
--------
>>> import numpy.polynomial.chebyshev as C
>>> C.chebinterpolate(lambda x: np.tanh(x) + 0.5, 8)
array([  5.00000000e-01,   8.11675684e-01,  -9.86864911e-17,
        -5.42457905e-02,  -2.71387850e-16,   4.51658839e-03,
         2.46716228e-17,  -3.79694221e-04,  -3.26899002e-16])

Notes
-----
The Chebyshev polynomials used in the interpolation are orthogonal when
sampled at the Chebyshev points of the first kind. If it is desired to
constrain some of the coefficients they can simply be set to the desired
value after the interpolation, no new interpolation or fit is needed. This
is especially useful if it is known apriori that some of coefficients are
zero. For instance, if the function is even then the coefficients of the
terms of odd degree in the result can be set to zero.

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