class_docstr: LoRA incorporated in Embedding Layer. Weights of embedding layer are set to be frozen per default.
  
  Parameters
  ----------
  num_embeddings
      size of the dictionary of embeddings.
  embedding_dim
       the size of each embedding vector.
  r
      rank r of the low-rank decomposition.
  lora_alpha
      Scaling factor. Can be simply set to same value as r as initialization is scaled already.
  merge_weights
      Merging weights during inference to reduce latency.
  
  References
  ----------
  1. Edward J. Hu*, Yelong Shen*, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen,
  "LoRA: Low-Rank Adaptation of Large Language Models", 2021
  https://arxiv.org/abs/2106.09685
  2. Code: https://github.com/microsoft/LoRA
code: def eval(self):
      nn.Linear.eval(self)
      if self.merge_weights and not self.merged:
          # Merge the weights and mark it
          if self.r > 0:
              self.weight.data += (
                  self.lora_B @ self.lora_A
              ) * self.scaling
          self.merged = True
  
imports: import math
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 4 for calling the LoRAEmbedding obj's underlying member method code with example usage: obj.eval() without return types