class_docstr: Hyper-network allowing f(z(t), t) to change with time.
  
  Adapted from the NumPy implementation at:
  https://gist.github.com/rtqichen/91924063aa4cc95e7ef30b3a5491cc52
class_docstr_tok: ["Hyper-network","allowing","f","(","z","(","t",")",",","t",")","to","change","with","time",".","Adapted","from","the","NumPy","implementation","at",":","https",":","\/\/gist.github.com\/rtqichen\/91924063aa4cc95e7ef30b3a5491cc52"]
code: def forward(self, t):
      # predict params
      params = t.reshape(1, 1)
      params = torch.tanh(self.fc1(params))
      params = torch.tanh(self.fc2(params))
      params = self.fc3(params)
  
      # restructure
      params = params.reshape(-1)
      W = params[: self.blocksize].reshape(
          self.width, self.in_out_dim, 1
      )
  
      U = params[self.blocksize : 2 * self.blocksize].reshape(
          self.width, 1, self.in_out_dim
      )
  
      G = params[2 * self.blocksize : 3 * self.blocksize].reshape(
          self.width, 1, self.in_out_dim
      )
      U = U * torch.sigmoid(G)
  
      B = params[3 * self.blocksize :].reshape(self.width, 1, 1)
      return [W, B, U]
  
imports: import os
  import argparse
  import glob
  from PIL import Image
  import numpy
  import matplotlib
  import matplotlib.pyplot
  from sklearn.datasets import make_circles
  import torch
  import torch.nn
  import torch.optim
label_desc: Use image node_id 2 for calling the HyperNetwork obj's underlying member method code with example usage: obj.forward(t) and returns: list