code: def forward(self, x):
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      out = self.pool(self.model(x))
      out = torch.flatten(out, start_dim=2)
      out = out.transpose(1, 2).contiguous()
      return out
  
imports: import json
  import os
  from collections import Counter
  import torch
  import torchvision
  import torchvision.transforms
  from PIL import Image
  from torch import nn
  from torch.utils.data import Dataset
label_desc: Use image node_id 2 for calling the ImageEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: out