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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 get_string(self, betas: List[float], gammas: List[float], samples: int = 100):
 """ Compute the most probable string. The method assumes you have passed init_betas and init_gammas with your pre-computed angles or you have run the VQE loop to determine the angles. If you have not done this you will be returning the output for a random set of angles. :param betas: List of beta angles :param gammas: List of gamma angles :param samples: (Optional) number of samples to get back from the QuantumComputer. :returns: tuple representing the bitstring, Counter object from collections holding all output bitstrings and their frequency. """

        if samples <= 0 and not isinstance(samples, int):
            raise ValueError("samples variable must be positive integer")
        param_prog = self.get_parameterized_program()
        stacked_params = np.hstack((betas, gammas))

        sampling_prog = Program()
        ro = sampling_prog.declare('ro', 'BIT', len(self.qubits))
        sampling_prog += param_prog(stacked_params)
        sampling_prog += [MEASURE(qubit, r) for qubit, r in zip(self.qubits, ro)]
        sampling_prog.wrap_in_numshots_loop(samples)
        executable = self.qc.compile(sampling_prog)
        bitstring_samples = self.qc.run(executable)
        bitstring_tuples = list(map(tuple, bitstring_samples))
        freq = Counter(bitstring_tuples)
        most_frequent_bit_string = max(freq, key=lambda x: freq[x])
        return most_frequent_bit_string, freq