class_docstr: Builds a hypernetwork that predicts a fully connected neural network.
class_docstr_tok: ["Builds","a","hypernetwork","that","predicts","a","fully","connected","neural","network","."]
docstr: :param hyper_input: Input to hypernetwork.
  :return: nn.Module; Predicted fully connected neural network.
docstr_tok: [":","param","hyper_input",":","Input","to","hypernetwork",".",":","return",":","nn.Module",";","Predicted","fully","connected","neural","network","."]
code: def forward(self, hyper_input):
      """
      :param hyper_input: Input to hypernetwork.
      :return: nn.Module; Predicted fully connected neural network.
      """
      net = []
      for i in range(len(self.layers)):
          net.append(self.layers[i](hyper_input))
  
      return nn.Sequential(*net)
  
imports: import functools
  import torch
  import torch.nn
  from .None import pytorch_prototyping
label_desc: Use image node_id 2 for calling the HyperFC obj's underlying member method code with example usage: obj.forward(hyper_input) and returns: nn