Priors of tested media channels about lightweight_mmm HOT 3 OPEN

def _preprocess_custom_priors(
      self,
      custom_priors: Dict[str, Prior]) -> MutableMapping[str, Prior]:
    """Preprocesses the user input custom priors to Numpyro distributions.


    If numpyro distributions are given they remains untouched, however if any
    other option is passed, it is passed to the default distribution to alter
    its constructor values.

    Args:
      custom_priors: Mapping of the name of the prior to its custom value.

    Returns:
      A mapping of names to numpyro distributions based on user input and
        default values.
    """

 """
    default_priors = {
        **models._get_default_priors(),
        **models._get_transform_default_priors()[self.model_name]
    }
    # Checking that the key is contained in custom_priors has already been done
    # at this point in the fit function.
    for prior_name in custom_priors:
      if isinstance(custom_priors[prior_name], numbers.Number):
        custom_priors[prior_name] = default_priors[prior_name].__class__(
            custom_priors[prior_name])
      elif (isinstance(custom_priors[prior_name], collections.abc.Sequence) and
            not isinstance(custom_priors[prior_name], str)):
        custom_priors[prior_name] = default_priors[prior_name].__class__(
            *custom_priors[prior_name])
      elif isinstance(custom_priors[prior_name], dict):
        custom_priors[prior_name] = default_priors[prior_name].__class__(
            **custom_priors[prior_name])
      elif not isinstance(custom_priors[prior_name], dist.Distribution):
        raise ValueError(
            "Priors given must be a Numpyro distribution or one of the "
            "following to fit in the constructor of our default Numpyro "
            "distribution. It could be given as args or kwargs as long as it "
            "is the correct format for such object. Please refer to our "
            "documentation on custom priors to know more.")
    return custom_priors