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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 inv(self, q_data, max_iterations=100, tollerance=1e-5):
 """ Inverse Rosenblatt transformation. If possible the transformation is done analytically. If not possible, transformation is approximated using an algorithm that alternates between Newton-Raphson and binary search. Args: q_data (numpy.ndarray):
 Probabilities to be inverse. If any values are outside ``[0, 1]``, error will be raised. ``q_data.shape`` must be compatible with distribution shape. max_iterations (int):
 If approximation is used, this sets the maximum number of allowed iterations in the Newton-Raphson algorithm. tollerance (float):
 If approximation is used, this set the error tolerance level required to define a sample as converged. Returns: (numpy.ndarray):
 Inverted probability values where ``out.shape == q_data.shape``. """

        q_data = numpy.asfarray(q_data)
        assert numpy.all((q_data >= 0) & (q_data <= 1)), "sanitize your inputs!"
        shape = q_data.shape
        q_data = q_data.reshape(len(self), -1)
        x_data = evaluation.evaluate_inverse(self, q_data)
        lower, upper = evaluation.evaluate_bound(self, x_data)
        x_data = numpy.clip(x_data, a_min=lower, a_max=upper)
        x_data = x_data.reshape(shape)
        return x_data