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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 fixed_gaussian_prior_builder( getter, name, dtype=None, *args, **kwargs):
 """A pre-canned builder for fixed gaussian prior distributions. Given a true `getter` function and arguments forwarded from `tf.get_variable`, return a distribution object for a scalar-valued fixed gaussian prior which will be broadcast over a variable of the requisite shape. 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`. dtype: The `dtype` argument passed to `tf.get_variable`. *args: See positional arguments passed to `tf.get_variable`. **kwargs: See keyword arguments passed to `tf.get_variable`. Returns: An instance of `tfp.distributions.Normal` representing the prior distribution over the variable in question. """

  del getter  # Unused.
  del args  # Unused.
  del kwargs  # Unused.
  loc = tf.constant(0.0, shape=(), dtype=dtype)
  scale = tf.constant(0.01, shape=(), dtype=dtype)
  return tfp.distributions.Normal(
      loc=loc, scale=scale, name="{}_prior_dist".format(name))