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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _copy_convert(X, const=None, remove_mean=False, copy=True):
 r""" Makes a copy or converts the data type if needed Copies the data and converts the data type if unsuitable for covariance calculation. The standard data type for covariance computations is float64, because the double precision (but not single precision) is usually sufficient to compute the long sums involved in covariance matrix computations. Integer types are avoided even if the data is integer, because the BLAS matrix multiplication is very fast with floats, but very slow with integers. If X is of boolean type (0/1), the standard data type is float32, because this will be sufficient to represent numbers up to 2^23 without rounding error, which is usually sufficient sufficient as the largest element in np.dot(X.T, X) can then be T, the number of data points. Parameters remove_mean : bool If True, will enforce float64 even if the input is boolean copy : bool If True, enforces a copy even if the data type doesn't require it. Return ------ X : ndarray copy or reference to X if no copy was needed. const : ndarray or None copy or reference to const if no copy was needed. """

    # determine type
    dtype = np.float64  # default: convert to float64 in order to avoid cancellation errors
    if X.dtype.kind == 'b' and X.shape[0] < 2**23 and not remove_mean:
        dtype = np.float32  # convert to float32 if we can represent all numbers
    # copy/convert if needed
    if X.dtype not in (np.float64, dtype):  # leave as float64 (conversion is expensive), otherwise convert to dtype
        X = X.astype(dtype, order='C')
        if const is not None:
            const = const.astype(dtype, order='C')
    elif copy:
        X = X.copy(order='C')
        if const is not None:
            const = const.copy(order='C')

    return X, const