code: def __init__(self, args, width_factor=1):
      super(PrivateEncoder, self).__init__()
      self.args = args
      input_dim = args.num_chan
      patch_size = args.private_kernel_size
      output_dim = args.hidden_dim
      num_patches = (args.img_size[0] // patch_size) ** 2
      self.noise_size = 1
  
      args.input_dim = args.hidden_dim
  
      layers = [
          nn.Conv2d(
              input_dim,
              output_dim * width_factor,
              kernel_size=patch_size,
              dilation=1,
              stride=patch_size,
          ),
          nn.ReLU(),
      ]
      for _ in range(self.args.private_depth):
          layers.extend(
              [
                  nn.Conv2d(
                      output_dim * width_factor,
                      output_dim * width_factor,
                      kernel_size=1,
                      dilation=1,
                      stride=1,
                  ),
                  nn.BatchNorm2d(
                      output_dim * width_factor,
                      track_running_stats=False,
                  ),
                  nn.ReLU(),
              ]
          )
  
      self.image_encoder = nn.Sequential(*layers)
  
      self.pos_embedding = nn.Parameter(
          torch.randn(1, num_patches, output_dim * width_factor)
      )
  
      self.mixer = nn.Sequential(
          *[nn.ReLU(), nn.Linear(output_dim * width_factor, output_dim)]
      )
  
imports: import collections
  import hashlib
  import time
  import multiprocessing
  import torch
  import numpy
  import torch.nn
  import scipy.stats
  import matplotlib.pyplot
  from PIL import Image
  import jax
  import jax.numpy
  import objax
  import scipy.optimize
  import numpy
  import multiprocessing
label_desc: Use image node_id 1 to create a new PrivateEncoder object from inherited base classes: nn with example: obj = PrivateEncoder(args, width_factor)