class_docstr: Abstract specification of a term prior.
  
  Parameters
  ----------
  name : str
      Name of prior distribution. Must be the name of a PyMC distribution
      (e.g., ``"Normal"``, ``"Bernoulli"``, etc.)
  auto_scale: bool
      Whether to adjust the parameters of the prior or use them as passed. Default to ``True``.
  kwargs : dict
      Optional keywords specifying the parameters of the named distribution.
  dist : pymc.distributions.distribution.DistributionMeta or callable
      A callable that returns a valid PyMC distribution. The signature must contain ``name``,
      ``dims``, and ``shape``, as well as its own keyworded arguments.
code: def __str__(self):
      args = ", ".join(
          [
              f"{k}: {format_arg(v, 4)}"
              if not isinstance(v, type(self))
              else f"{k}: {v}"
              for k, v in self.args.items()
          ]
      )
      return f"{self.name}({args})"
  
file_path: bambi/bambi/priors/prior.py
imports: import numpy
  import pytensor
label_desc: Use image node_id 4 for calling the Prior obj's underlying member method code with example usage: obj.__str__() and returns: self, str, args, str