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.
docstr: Generate samples from the scipy uniform distribution.
  
  Args:
      n: Number of samples to generate.
      tunable_d: Dimension of samples to generate.
  
  Returns:
      samples: An (n x d) array of random points.
docstr_tok: ["Generate","samples","from","the","scipy","uniform","distribution",".","Args",":","n",":","Number","of","samples","to","generate",".","tunable_d",":","Dimension","of","samples","to","generate",".","Returns",":","samples",":","An","(","n","x","d",")","array","of","random","points","."]
code: def _gen_samples(self, n: int, tunable_d: int) -> np.ndarray:
      """Generate samples from the scipy uniform distribution.
  
      Args:
          n: Number of samples to generate.
          tunable_d: Dimension of samples to generate.
  
      Returns:
          samples: An (n x d) array of random points.
  
      """
      return uniform.rvs(size=(n, tunable_d), random_state=self._rs)
  
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 2 for calling the UniformGenerator obj's underlying member method code with example usage: obj._gen_samples(n, tunable_d) and returns: uniform