class_docstr: Uniform random sampling pseudo-optimiser.
docstr: Sample uniformly the domain for `batch_size` number of times.
  
  Args:
      batch_size: (optional) :obj:`int`. The number of samples to return
          at one step.
  
  Returns:
      A list of `batch_size` many :class:`Sample` instances.
docstr_tok: ["Sample","uniformly","the","domain","for","`","batch_size","`","number","of","times",".","Args",":","batch_size",":","(","optional",")",":","obj",":","`","int","`",".","The","number","of","samples","to","return","at","one","step",".","Returns",":","A","list","of","`","batch_size","`","many",":","class",":","`","Sample","`","instances","."]
code: def run_step(self, batch_size=1, **kwargs) -> List[Sample]:
      """Sample uniformly the domain for `batch_size` number of times.
  
      Args:
          batch_size: (optional) :obj:`int`. The number of samples to return
              at one step.
  
      Returns:
          A list of `batch_size` many :class:`Sample` instances.
      """
      return [self.domain.sample() for _ in range(batch_size)]
  
imports: from typing import List
  from hypertunity.domain import Domain, Sample
  from hypertunity.optimisation.base import Optimiser
label_desc: Use image node_id 2 for calling the RandomSearch obj's underlying member method code with example usage: obj.run_step(batch_size) and returns: unknown