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 __init__(self, name, auto_scale=True, dist=None, **kwargs):
      self.name = name
      self.auto_scale = auto_scale
      self.args = {}
      self.update(**kwargs)
      self.dist = dist
  
file_path: bambi/bambi/priors/prior.py
imports: import numpy
  import pytensor
label_desc: Use image node_id 1 to create a new Prior object  with example: obj = Prior(name, auto_scale, dist)