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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 energies(self, samples_like, dtype=np.float):
 """The energies of the given samples. Args: samples_like (samples_like):
 A collection of raw samples. `samples_like` is an extension of NumPy's array_like structure. See :func:`.as_samples`. dtype (:class:`numpy.dtype`, optional):
 The data type of the returned energies. Defaults to float. Returns: :obj:`numpy.ndarray`: The energies. """

        samples, labels = as_samples(samples_like)
        if labels:
            idx, label = zip(*enumerate(labels))
            labeldict = dict(zip(label, idx))
        else:
            labeldict = {}

        num_samples = samples.shape[0]

        energies = np.zeros(num_samples, dtype=dtype)
        for term, bias in self.items():
            if len(term) == 0:
                energies += bias
            else:
                energies += np.prod([samples[:, labeldict[v]] for v in term], axis=0) * bias

        return energies