code: def forward(self, x):
      # get representations
      f = self.backbone(x).flatten(start_dim=1)
      # get projections
      z = self.projection_head(f)
      # get predictions
      p = self.prediction_head(z)
      # stop gradient
      z = z.detach()
      return z, p
  
imports: import math
  import numpy
  import torch
  import torch.nn
  import torchvision
  from lightly.data import LightlyDataset
  from lightly.loss import NegativeCosineSimilarity
  from lightly.models.modules.heads import SimSiamPredictionHead, SimSiamProjectionHead
  from lightly.transforms import SimCLRTransform, utils
  import os
  import matplotlib.offsetbox
  import matplotlib.pyplot
  import torchvision.transforms.functional
  from matplotlib import rcParams
  from PIL import Image
  from sklearn import random_projection
label_desc: Use image node_id 2 for calling the SimSiam obj's underlying member method code with example usage: obj.forward(x) and returns: z,  p