class_docstr: This attack was originally implemented by Goodfellow et al. (2014) 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
  
  :param model: cleverhans.model.Model
  :param sess: optional tf.Session
  :param dtypestr: dtype of the data
  :param kwargs: passed through to super constructor
class_docstr_tok: ["This","attack","was","originally","implemented","by","Goodfellow","et","al",".","(","2014",")","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",":","param","model",":","cleverhans.model.Model",":","param","sess",":","optional","tf.Session",":","param","dtypestr",":","dtype","of","the","data",":","param","kwargs",":","passed","through","to","super","constructor"]
docstr: Create a FastGradientMethod instance.
  Note: the model parameter should be an instance of the
  cleverhans.model.Model abstraction provided by CleverHans.
docstr_tok: ["Create","a","FastGradientMethod","instance",".","Note",":","the","model","parameter","should","be","an","instance","of","the","cleverhans.model.Model","abstraction","provided","by","CleverHans","."]
code: def __init__(self, model, sess=None, dtypestr="float32", **kwargs):
      """
      Create a FastGradientMethod instance.
      Note: the model parameter should be an instance of the
      cleverhans.model.Model abstraction provided by CleverHans.
      """
  
      super(FastGradientMethod, self).__init__(
          model, sess, dtypestr, **kwargs
      )
      self.feedable_kwargs = (
          "eps",
          "y",
          "y_target",
          "clip_min",
          "clip_max",
      )
      self.structural_kwargs = [
          "ord",
          "sanity_checks",
          "clip_grad",
          "loss_fn",
      ]
  
file_path: cleverhans/cleverhans_v3.1.0/cleverhans/attacks/fast_gradient_method.py
imports: import warnings
  import numpy
  import tensorflow
  from cleverhans.attacks.attack import Attack
  from cleverhans.compat import reduce_max, reduce_sum, softmax_cross_entropy_with_logits
  from cleverhans import utils_tf
label_desc: Use image node_id 1 to create a new FastGradientMethod object from inherited base classes: Attack with example: obj = FastGradientMethod(model, sess, dtypestr)