class_docstr: ## Generator Loss
  
  We want to find $   heta$ to minimize
  $$\mathbb{E}_{x \sim \mathbb{P}_r} [f_w(x)]- \mathbb{E}_{z \sim p(z)} [f_w(g_       heta(z))]$$
  The first component is independent of $     heta$,
  so we minimize,
  $$-rac{1}{m} \sum_{i=1}^m f_w ig( g_      heta(z^{(i)}) ig)$$
docstr: * `f_fake` is $f_w(g_   heta(z))$
code: def forward(self, f_fake: torch.Tensor):
      """
      * `f_fake` is $f_w(g_\theta(z))$
      """
      return -f_fake.mean()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/gan/wasserstein/__init__.py
imports: import torch.utils.data
  from torch.nn import functional
  from labml_helpers.module import Module
label_desc: Use image node_id 1 for calling the GeneratorLoss obj's underlying member method code with example usage: obj.forward(f_fake) and returns: unknown