code: def __init__(self, with_transform: bool) -> None:
      super().__init__()
  
      prior = tfp.distributions.Normal(
          to_default_float(1.0), to_default_float(1.0)
      )
  
      scale = np.exp(self.log_scale)
      if with_transform:
          transform = tfp.bijectors.Shift(to_default_float(0.0))(
              tfp.bijectors.Scale(to_default_float(scale))
          )
      else:
          transform = None
  
      self.theta = gpflow.Parameter(
          self.value, prior=prior, transform=transform
      )
  
imports: from typing import Any, Tuple, Type
  import numpy
  import pytest
  import tensorflow
  import tensorflow_probability
  from tensorflow_probability.python.bijectors import Exp
  from tensorflow_probability.python.distributions import Uniform
  import gpflow
  from gpflow.base import AnyNDArray, PriorOn
  from gpflow.config import set_default_float
  from gpflow.utilities import to_default_float
label_desc: Use image node_id 1 to create a new DummyModel object from inherited base classes: gpflow.models with example: obj = DummyModel(with_transform)