code: def gen_attack(
      predict_fn,
      loss_fn,
      x,
      y,
      eps,
      projector,
      nb_samples=100,
      nb_iter=40,
      tau=0.1,
      alpha_init=0.4,
      rho_init=0.5,
      decay=0.9,
      pop_init=None,
      scheduler=None,
      targeted=False,
  ):
      """
      Use a genetic algorithm to iteratively maximize the loss over the input,
      while staying within eps of the original input (using a projector).
  
      Used as part of GenAttack.
  
      :param predict: forward pass function.
      :param loss_fn: loss function
          - must accept tensors of shape [nbatch, pop_size, ndim]
      :param x: input tensor.
      :param y: label tensor.
          - if None and self.targeted=False, compute y as predicted
          labels.
          - if self.targeted=True, then y must be the targeted labels.
      :param eps: maximum distortion.
      :param projector: function to project the perturbation into the eps-ball
          - must accept tensors of shape [nbatch, pop_size, ndim]
      :param nb_samples: population size (default 100)
      :param nb_iter: number of iterations (default 40)
      :param tau: sampling temperature (default 0.1)
      :param alpha_init: initial mutation range (default 0.4)
      :param rho_init: initial probability for mutation (default 0.5)
      :param decay: decay param for scheduler (default 0.9)
      :param pop_init: initial population for genetic alg (default None)
      :param scheduler: initial state of scheduler(default None)
      :param targeted: if the attack is targeted (default False)
      """
      n_batch, n_dim = x.shape
  
      # [B,F]
      if pop_init is None:
          # Sample from Uniform(-1, 1)
          # shape: [B, N, F]
          pop_t = 2 * torch.rand(n_batch, nb_samples, n_dim) - 1
          # Sample from Uniform(-eps, eps)
          pop_t = eps[:, None, None] * pop_t
          pop_t = pop_t.to(x.device)
      else:
          pop_t = pop_init.clone()
  
      if scheduler is None:
          scheduler = GenAttackScheduler(x, alpha_init, rho_init, decay)
  
      inds = torch.arange(n_batch).to(x.device)
  
      for _ in range(nb_iter):
          adv = x[:, None, :] + pop_t
          # shape: [B, N]
          fitness = compute_fitness(
              predict_fn, loss_fn, adv, y, targeted=targeted
          )
          # shape: [1, B, 1]
          elite_val, elite_ind = fitness.max(-1)
          # shape: [B, F]
          elite_adv = adv[inds, elite_ind, :]
  
          # select which members will move onto the next generation
          # shape: [B, N]
          children = selection(pop_t, fitness, tau)
  
          # apply mutations and clipping
          # add mutated child to next generation (ie update pop_t)
          pop_t = mutation(
              children, scheduler.alpha, scheduler.rho, eps
          )
          pop_t = projector(pop_t)
  
          # Update params based on plateaus
          scheduler.update(elite_val)
  
      return elite_adv, pop_t, scheduler
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from typing import Optional
  from math import inf
  import torch
  import torch.nn.functional
  from advertorch.attacks.base import Attack
  from advertorch.attacks.base import LabelMixin
  from .utils import _check_param, _flatten, _make_projector
label_desc: Use image node_id 6 for calling a global function with example usage: gen_attack(predict_fn, loss_fn, x, y, eps, projector, nb_samples, nb_iter, tau, alpha_init, rho_init, decay, pop_init, scheduler, targeted) and returns: elite_adv,  pop_t,  scheduler