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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 sample(self, bqm, apply_flux_bias_offsets=True, **kwargs):
 """Sample from the given Ising model. Args: h (list/dict):
 Linear biases of the Ising model. If a list, the list's indices are used as variable labels. J (dict of (int, int):
float):
 Quadratic biases of the Ising model. apply_flux_bias_offsets (bool, optional):
 If True, use the calculated flux_bias offsets (if available). **kwargs: Optional keyword arguments for the sampling method, specified per solver. Examples: This example uses :class:`.VirtualGraphComposite` to instantiate a composed sampler that submits an Ising problem to a D-Wave solver selected by the user's default :std:doc:`D-Wave Cloud Client configuration file <cloud-client:intro>`. The problem represents a logical NOT gate using penalty function :math:`P = xy`, where variable x is the gate's input and y the output. This simple two-variable problem is manually minor-embedded to a single :std:doc:`Chimera <system:intro>` unit cell: each variable is represented by a chain of half the cell's qubits, x as qubits 0, 1, 4, 5, and y as qubits 2, 3, 6, 7. The chain strength is set to half the maximum allowed found from querying the solver's extended J range. In this example, the ten returned samples all represent valid states of the NOT gate. [-2.0, 1.0] {'y': -1, 'x': 1} {'y': 1, 'x': -1} {'y': -1, 'x': 1} {'y': -1, 'x': 1} {'y': -1, 'x': 1} {'y': 1, 'x': -1} {'y': 1, 'x': -1} {'y': 1, 'x': -1} {'y': -1, 'x': 1} {'y': 1, 'x': -1} See `Ocean Glossary <https://docs.ocean.dwavesys.com/en/latest/glossary.html>`_ for explanations of technical terms in descriptions of Ocean tools. """

        child = self.child

        if apply_flux_bias_offsets:
            if self.flux_biases is not None:
                kwargs[FLUX_BIAS_KWARG] = self.flux_biases

        return child.sample(bqm, **kwargs)