class_docstr: Uniform randomly sampled hyperparameter object.
class_docstr_tok: ["Uniform","randomly","sampled","hyperparameter","object","."]
docstr: Create a parameter to be sampled by the SearchStrategy
  
  :param str name: The parameter name. Match the Task hyperparameter name.
  :param float min_value: The minimum sample to use for uniform random sampling.
  :param float max_value: The maximum sample to use for uniform random sampling.
  :param float step_size: If not ``None``, set step size (quantization) for value sampling.
  :param bool include_max_value: Range includes the ``max_value``.
      The values are:
  
    - ``True`` - The range includes the ``max_value`` (Default)
    - ``False`` -  Does not include.
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code: def __init__(
      self,
      name,  # type: str
      min_value,  # type: float
      max_value,  # type: float
      step_size=None,  # type: Optional[float]
      include_max_value=True,  # type: bool
  ):
      # type: (...) -> ()
      """
      Create a parameter to be sampled by the SearchStrategy
  
      :param str name: The parameter name. Match the Task hyperparameter name.
      :param float min_value: The minimum sample to use for uniform random sampling.
      :param float max_value: The maximum sample to use for uniform random sampling.
      :param float step_size: If not ``None``, set step size (quantization) for value sampling.
      :param bool include_max_value: Range includes the ``max_value``.
          The values are:
  
        - ``True`` - The range includes the ``max_value`` (Default)
        - ``False`` -  Does not include.
  
      """
      super(UniformParameterRange, self).__init__(name=name)
      self.min_value = float(min_value)
      self.max_value = float(max_value)
      self.step_size = (
          float(step_size) if step_size is not None else None
      )
      self.include_max = include_max_value
  
imports: import sys
  from itertools import product
  from random import Random
  from typing import Mapping, Any, Sequence, Optional, Union
label_desc: Use image node_id 1 to create a new UniformParameterRange object from inherited base classes: Parameter with example: obj = UniformParameterRange(name, min_value, max_value, step_size, include_max_value)