chunk_content: s)
          embeddings = self.mean_pooling(model_output, kwargs["attention_mask"])
          if self.normalize:
              embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
  
          return embeddings
  
      def mean_pooling(self, model_output, attention_mask):
          token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
          input_mask_expanded = attention_mask.unsqueeze(-1).expand(to