class_docstr: Base class for representing parameter ranges.
  
  This is used to define what hyperparameters to tune for an Amazon SageMaker
  hyperparameter tuning job and to verify hyperparameters for Marketplace Algorithms.
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docstr: Initialize a parameter range.
  
  Args:
      min_value (float or int or PipelineVariable): The minimum value for the range.
      max_value (float or int or PipelineVariable): The maximum value for the range.
      scaling_type (str or PipelineVariable): The scale used for searching the range during
          tuning (default: 'Auto'). Valid values: 'Auto', 'Linear',
          'Logarithmic' and 'ReverseLogarithmic'.
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code: def __init__(
      self,
      min_value: Union[int, float, PipelineVariable],
      max_value: Union[int, float, PipelineVariable],
      scaling_type: Union[str, PipelineVariable] = "Auto",
  ):
      """Initialize a parameter range.
  
      Args:
          min_value (float or int or PipelineVariable): The minimum value for the range.
          max_value (float or int or PipelineVariable): The maximum value for the range.
          scaling_type (str or PipelineVariable): The scale used for searching the range during
              tuning (default: 'Auto'). Valid values: 'Auto', 'Linear',
              'Logarithmic' and 'ReverseLogarithmic'.
      """
      self.min_value = min_value
      self.max_value = max_value
      self.scaling_type = scaling_type
  
imports: from __future__ import absolute_import
  import json
  from typing import Union
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.utils import to_string
label_desc: Use image node_id 1 to create a new ParameterRange object from inherited base classes: object with example: obj = ParameterRange(min_value, max_value, scaling_type)