chunk_content: it__(self, model_name, tokenizer, normalize=True):
          super(AutoModelForSentenceEmbedding, self).__init__()
  
          self.model = AutoModel.from_pretrained(model_name)  
          self.normalize = normalize
          self.tokenizer = tokenizer
  
      def forward(self, **kwargs):
          model_output = self.model(**kwargs)
          embeddings = self.mean_pooling(model_output, kwargs["attention_mask"])
          if self.normalize:
              embeddi