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 __init__(self, in_out_dim, hidden_dim, width):
      super().__init__()
  
      blocksize = width * in_out_dim
  
      self.fc1 = nn.Linear(1, hidden_dim)
      self.fc2 = nn.Linear(hidden_dim, hidden_dim)
      self.fc3 = nn.Linear(hidden_dim, 3 * blocksize + width)
  
      self.in_out_dim = in_out_dim
      self.hidden_dim = hidden_dim
      self.width = width
      self.blocksize = blocksize
  
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 1 to create a new HyperNetwork object from inherited base classes: nn with example: obj = HyperNetwork(in_out_dim, hidden_dim, width)