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","."]
code: def __init__(
      self,
      hyper_in_ch,
      hyper_num_hidden_layers,
      hyper_hidden_ch,
      hidden_ch,
      num_hidden_layers,
      in_ch,
      out_ch,
      outermost_linear=False,
  ):
      super().__init__()
  
      PreconfHyperLinear = partialclass(
          HyperLinear,
          hyper_in_ch=hyper_in_ch,
          hyper_num_hidden_layers=hyper_num_hidden_layers,
          hyper_hidden_ch=hyper_hidden_ch,
      )
      PreconfHyperLayer = partialclass(
          HyperLayer,
          hyper_in_ch=hyper_in_ch,
          hyper_num_hidden_layers=hyper_num_hidden_layers,
          hyper_hidden_ch=hyper_hidden_ch,
      )
  
      self.layers = nn.ModuleList()
      self.layers.append(
          PreconfHyperLayer(in_ch=in_ch, out_ch=hidden_ch)
      )
  
      for i in range(num_hidden_layers):
          self.layers.append(
              PreconfHyperLayer(in_ch=hidden_ch, out_ch=hidden_ch)
          )
  
      if outermost_linear:
          self.layers.append(
              PreconfHyperLinear(in_ch=hidden_ch, out_ch=out_ch)
          )
      else:
          self.layers.append(
              PreconfHyperLayer(in_ch=hidden_ch, out_ch=out_ch)
          )
  
imports: import functools
  import torch
  import torch.nn
  from .None import pytorch_prototyping
label_desc: Use image node_id 1 to create a new HyperFC object from inherited base classes: nn with example: obj = HyperFC(hyper_in_ch, hyper_num_hidden_layers, hyper_hidden_ch, hidden_ch, num_hidden_layers, in_ch, out_ch, outermost_linear)