class_docstr: Inherited class from Attack and cleverhans.attacks.FastGradientMethod.
  
  This attack was originally implemented by Goodfellow et al. (2015) with the
  infinity norm (and is known as the "Fast Gradient Sign Method"). This
  implementation extends the attack to other norms, and is therefore called
  the Fast Gradient Method.
  Paper link: https://arxiv.org/abs/1412.6572
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docstr: Creates a FastGradientMethod instance in eager execution.
  :model: cleverhans.model.Model
  :dtypestr: datatype in the string format.
docstr_tok: ["Creates","a","FastGradientMethod","instance","in","eager","execution",".",":","model",":","cleverhans.model.Model",":","dtypestr",":","datatype","in","the","string","format","."]
code: def __init__(self, model, dtypestr="float32", **kwargs):
      """
      Creates a FastGradientMethod instance in eager execution.
      :model: cleverhans.model.Model
      :dtypestr: datatype in the string format.
      """
      del kwargs
      if not isinstance(model, Model):
          wrapper_warning()
          model = CallableModelWrapper(model, "probs")
  
      super(FastGradientMethod, 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 FastGradientMethod object from inherited base classes: Attack, attacks with example: obj = FastGradientMethod(model, dtypestr)