class_docstr: Wrapper around `crit_loss_func` and `gen_loss_func`
class_docstr_tok: ["Wrapper","around","`","crit_loss_func","`","and","`","gen_loss_func","`"]
docstr: Create some `fake_pred` with the generator from `input` and compare them to `real_pred` in `self.crit_loss_func`.
docstr_tok: ["Create","some","`","fake_pred","`","with","the","generator","from","`","input","`","and","compare","them","to","`","real_pred","`","in","`","self.crit_loss_func","`","."]
code: def critic(
      self,
      real_pred,  # Critic predictions for real images
      input,  # Input noise vector to pass into generator
  ):
      "Create some `fake_pred` with the generator from `input` and compare them to `real_pred` in `self.crit_loss_func`."
      fake = self.gan_model.generator(input).requires_grad_(False)
      fake_pred = self.gan_model.critic(fake)
      self.crit_loss = self.crit_loss_func(real_pred, fake_pred)
      return self.crit_loss
  
imports: from __future__ import annotations
  from ..basics import *
  from .all import *
label_desc: Use image node_id 3 for calling the GANLoss obj's underlying member method code with example usage: obj.critic(real_pred, input) and returns: self