code: def embed_similarity(
      key_embeds: Tensor,
      ref_embeds: Tensor,
      method: str = "dot_product",
      temperature: int = -1,
  ) -> Tensor:
      """Calculate feature similarity from embeddings.
  
      Args:
          key_embeds (Tensor): Shape (N1, C).
          ref_embeds (Tensor): Shape (N2, C).
          method (str, optional): Method to calculate the similarity,
              options are 'dot_product' and 'cosine'. Defaults to
              'dot_product'.
          temperature (int, optional): Softmax temperature. Defaults to -1.
  
      Returns:
          Tensor: Similarity matrix of shape (N1, N2).
      """
      assert method in ["dot_product", "cosine"]
  
      if method == "cosine":
          key_embeds = F.normalize(key_embeds, p=2, dim=1)
          ref_embeds = F.normalize(ref_embeds, p=2, dim=1)
  
      similarity = torch.mm(key_embeds, ref_embeds.T)
  
      if temperature > 0:
          similarity /= float(temperature)
      return similarity
  
imports: import torch
  import torch.nn.functional
  from torch import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: embed_similarity(key_embeds, ref_embeds, method, temperature) and returns: similarity