class_docstr: Base class for Deterministic distributions.
class_docstr_tok: ["Base","class","for","Deterministic","distributions","."]
code: def _parameter_control_dependencies(self, is_init):
      assertions = []
  
      # In init, we can always build shape and dtype checks because
      # we assume shape doesn't change for Variable backed args.
      if is_init and self._is_vector:
          msg = "Argument `loc` must be at least rank 1."
          if tensorshape_util.rank(self.loc.shape) is not None:
              if tensorshape_util.rank(self.loc.shape) < 1:
                  raise ValueError(msg)
          elif self.validate_args:
              assertions.append(
                  assert_util.assert_rank_at_least(
                      self.loc, 1, message=msg
                  )
              )
  
      if not self.validate_args:
          assert not assertions  # Should never happen
          return []
  
      if is_init != tensor_util.is_ref(self.atol):
          assertions.append(
              assert_util.assert_non_negative(
                  self.atol,
                  message='Argument "atol" must be non-negative',
              )
          )
      if is_init != tensor_util.is_ref(self.rtol):
          assertions.append(
              assert_util.assert_non_negative(
                  self.rtol,
                  message='Argument "rtol" must be non-negative',
              )
          )
      return assertions
  
imports: import abc
  import six
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 12 for calling the _BaseDeterministic obj's underlying member method code with example usage: obj._parameter_control_dependencies(is_init) and returns: assertions, list