class_docstr: An algorithm hyperparameter with optional validation.
  
  Implemented as a python descriptor object.
class_docstr_tok: ["An","algorithm","hyperparameter","with","optional","validation",".","Implemented","as","a","python","descriptor","object","."]
docstr: Args:
  
  name (str): The name of this hyperparameter validate
  (callable[object]->[bool]): A validation function or list of validation
  functions.
  
      Each function validates an object and returns False if the object
      value is invalid for this hyperparameter.
  
  validation_message (str): A usage guide to display on validation
  failure.
  
  Args:
      name:
      validate:
      validation_message:
      data_type:
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code: def __init__(
      self,
      name,
      validate=lambda _: True,
      validation_message="",
      data_type=str,
  ):
      """Args:
  
      name (str): The name of this hyperparameter validate
      (callable[object]->[bool]): A validation function or list of validation
      functions.
  
          Each function validates an object and returns False if the object
          value is invalid for this hyperparameter.
  
      validation_message (str): A usage guide to display on validation
      failure.
  
      Args:
          name:
          validate:
          validation_message:
          data_type:
      """
      self.validation = validate
      self.validation_message = validation_message
      self.name = name
      self.data_type = data_type
      try:
          iter(self.validation)
      except TypeError:
          self.validation = [self.validation]
  
file_path: sagemaker-python-sdk/src/sagemaker/amazon/hyperparameter.py
imports: from __future__ import absolute_import
  import json
  from sagemaker.workflow import is_pipeline_variable
label_desc: Use image node_id 1 to create a new Hyperparameter object from inherited base classes: object with example: obj = Hyperparameter(name, validate, validation_message, data_type)