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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 custom_scale_mixture_prior_builder(getter, name, *args, **kwargs):
 """A builder for the gaussian scale-mixture prior of Fortunato et al. Please see https://arxiv.org/abs/1704.02798, section 7.1 Args: getter: The `getter` passed to a `custom_getter`. Please see the documentation for `tf.get_variable`. name: The `name` argument passed to `tf.get_variable`. *args: Positional arguments forwarded by `tf.get_variable`. **kwargs: Keyword arguments forwarded by `tf.get_variable`. Returns: An instance of `tfp.distributions.Distribution` representing the prior distribution over the variable in question. """

  # This specific prior formulation doesn't need any of the arguments forwarded
  # from `get_variable`.
  del getter
  del name
  del args
  del kwargs
  return CustomScaleMixture(
      FLAGS.prior_pi, FLAGS.prior_sigma1, FLAGS.prior_sigma2)