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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 make_epochs_per_sample(weights, n_epochs):
 """Given a set of weights and number of epochs generate the number of epochs per sample for each weight. Parameters weights: array of shape (n_1_simplices) The weights ofhow much we wish to sample each 1-simplex. n_epochs: int The total number of epochs we want to train for. Returns ------- An array of number of epochs per sample, one for each 1-simplex. """

    result = -1.0 * np.ones(weights.shape[0], dtype=np.float64)
    n_samples = n_epochs * (weights / weights.max())
    result[n_samples > 0] = float(n_epochs) / n_samples[n_samples > 0]
    return result