code: def encode_text(self, text):
      x = self.token_embedding(text)  # [batch_size, n_ctx, d_model]
      x = x + self.positional_embedding
      x = x.permute(1, 0, 2)  # NLD -> LND
      x = self.transformer(x)
      x = x.permute(1, 0, 2)  # LND -> NLD
      x = self.ln_final(x)
  
      # take features from the eot embedding (eot_token is the highest number in each sequence)
      x = (
          x[torch.arange(x.shape[0]), text.argmax(dim=-1)]
          @ self.text_projection
      )
  
      return x
  
imports: from collections import OrderedDict
  import timm
  from torch import nn
  from lavis.models.ulip_models import losses
  from torch.nn.parameter import Parameter
  from easydict import EasyDict
  import torch
  import numpy
  from lavis.common.dist_utils import download_cached_file
label_desc: Use image node_id 3 for calling the ULIP_WITH_IMAGE obj's underlying member method code with example usage: obj.encode_text(text) and returns: x