class_docstr: Text to vision encoding by Contrastive Language-Image Pre-training (CLIP) or random embedding.
  The text to vision encoder loads the pre-trained or random initialized weights with connection to 2D/3D vision models.
  
  Contrastive Language-Image Pre-training (CLIP), based on: "Radford et al.,
  Learning Transferable Visual Models From Natural Language Supervision <https://arxiv.org/abs/2103.00020>"
  
  Connecting text and medical 3D image, based on: "Liu et al.,
  CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection <https://arxiv.org/pdf/2301.00785.pdf>"
code: def forward(self):
      if self.encoding == "rand_embedding":
          # text embedding as random initialized 'rand_embedding'
          text_embedding = self.text_embedding.weight
      else:
          print(self.text_embedding)
          text_embedding = nn.functional.relu(
              self.text_to_vision(self.text_embedding)
          )
  
      if self.spatial_dims == 3:
          text_embedding = (
              text_embedding.unsqueeze(2).unsqueeze(2).unsqueeze(2)
          )
      elif self.spatial_dims == 2:
          text_embedding = text_embedding.unsqueeze(2).unsqueeze(2)
  
      return text_embedding
  
file_path: MONAI/monai/networks/blocks/text_embedding.py
imports: from __future__ import annotations
  import torch
  from torch import nn
  from torch.utils import model_zoo
label_desc: Use image node_id 2 for calling the TextEncoder obj's underlying member method code with example usage: obj.forward() and returns: text_embedding