class_docstr: Inherited class from Attack and cleverhans.attacks.BasicIterativeMethod.
  
  The Basic Iterative Method (Kurakin et al. 2016). The original paper used
  hard labels for this attack; no label smoothing.
  Paper link: https://arxiv.org/pdf/1607.02533.pdf
class_docstr_tok: ["Inherited","class","from","Attack","and","cleverhans.attacks.BasicIterativeMethod",".","The","Basic","Iterative","Method","(","Kurakin","et","al",".","2016",")",".","The","original","paper","used","hard","labels","for","this","attack",";","no","label","smoothing",".","Paper","link",":","https",":","\/\/arxiv.org\/pdf\/1607.02533.pdf"]
docstr: Creates a BasicIterativeMethod instance in eager execution.
  :param model: cleverhans.model.Model
  :param dtypestr: datatype in the string format.
docstr_tok: ["Creates","a","BasicIterativeMethod","instance","in","eager","execution",".",":","param","model",":","cleverhans.model.Model",":","param","dtypestr",":","datatype","in","the","string","format","."]
code: def __init__(self, model, dtypestr="float32"):
      """
      Creates a BasicIterativeMethod instance in eager execution.
      :param model: cleverhans.model.Model
      :param dtypestr: datatype in the string format.
      """
      if not isinstance(model, Model):
          wrapper_warning()
          model = CallableModelWrapper(model, "probs")
  
      super(BasicIterativeMethod, self).__init__(model, dtypestr)
  
imports: from distutils.version import LooseVersion
  import numpy
  import tensorflow
  from cleverhans import attacks
  from cleverhans import utils
  from cleverhans.model import CallableModelWrapper, wrapper_warning
  from cleverhans.model import Model
  from cleverhans.loss import LossCrossEntropy
label_desc: Use image node_id 1 to create a new BasicIterativeMethod object from inherited base classes: Attack, attacks with example: obj = BasicIterativeMethod(model, dtypestr)