code: def forward(self, x):
      x = self.conv_features(x)  # [b, d, h, w]
      b, d, h, w = x.shape
      if self.use_transformer:
          # add positional encodings
          y = torch.stack(
              [
                  torch.cat([torch.arange(h).unsqueeze(1)] * w, dim=1),
                  torch.cat([torch.arange(w).unsqueeze(0)] * h, dim=0),
              ]
          )  # [2, h, w
          y = y.view([2, h * w]).transpose(1, 0)  # [h*w, 2]
          y = y.type(torch.float32).to(flair.device)
          y = (
              self.position_features(y).transpose(1, 0).view([d, h, w])
          )  # [h*w, d] => [d, h, w]
          y = y.unsqueeze(dim=0)  # [1, d, h, w]
          x = x + y  # [b, d, h, w] + [1, d, h, w] => [b, d, h, w]
          # reshape the pixels into the sequence
          x = x.view([b, d, h * w])  # [b, d, h*w]
          # layer norm after convolution and positional encodings
          x = F.layer_norm(x.permute([0, 2, 1]), (d,)).permute(
              [0, 2, 1]
          )
          # add <cls> token
          x = torch.cat(
              [x, torch.stack([self.cls_token] * b)], dim=2
          )  # [b, d, h*w+1]
          # transformer requires input in the shape [h*w+1, b, d]
          x = (
              x.view([b * d, h * w + 1])
              .transpose(1, 0)
              .view([h * w + 1, b, d])
          )  # [b, d, h*w+1] => [b*d, h*w+1] => [h*w+1, b*d] => [h*w+1, b*d]
          x = self.transformer(x)  # [h*w+1, b, d]
          # the output is an embedding of <cls> token
          x = x[-1, :, :]  # [b, d]
      else:
          x = x.view([-1, self._feat_dim])
          x = F.layer_norm(x, (self._feat_dim,))
  
      return x
  
imports: import logging
  from typing import Any, Dict, List, Optional
  import torch
  import torch.nn.functional
  from torch.nn import AdaptiveAvgPool2d, AdaptiveMaxPool2d, Conv2d, Dropout2d, Linear, MaxPool2d, Parameter, ReLU, Sequential, TransformerEncoder, TransformerEncoderLayer
  import flair
  from flair.data import Image
  from flair.embeddings.base import Embeddings, register_embeddings
label_desc: Use image node_id 2 for calling the ConvTransformNetworkImageEmbeddings obj's underlying member method code with example usage: obj.forward(x) and returns: x