class_docstr: This class specifies a uniform random generation algorithm.
  
  As a uniform generator does not make use of a model, it does not implement
  the fit or predict methods.
  
  See base `RandomModel` for a description of model attributes.
code: def __init__(
      self,
      deduplicate: bool = True,
      seed: Optional[int] = None,
      generated_points: Optional[np.ndarray] = None,
      fallback_to_sample_polytope: bool = False,
  ) -> None:
      super().__init__(
          deduplicate=deduplicate,
          seed=seed,
          generated_points=generated_points,
          fallback_to_sample_polytope=fallback_to_sample_polytope,
      )
      self._rs = np.random.RandomState(seed=self.seed)
  
imports: from typing import Optional
  import numpy
  from ax.models.random.base import RandomModel
  from scipy.stats import uniform
label_desc: Use image node_id 1 to create a new UniformGenerator object from inherited base classes: RandomModel with example: obj = UniformGenerator(deduplicate, seed, generated_points, fallback_to_sample_polytope)