code: def getEuclideanDistance(
      specEmbA: torch.Tensor,
      specEmbB: torch.Tensor,
      device: torch.device = torch.device("cpu"),
  ) -> torch.Tensor:
      """
      Calculate Euclidean distances from the given feature tensors.
  
      Args:
          specEmbA (Tensor):
              Matrix containing spectral embedding vectors from eigenvalue decomposition (N x embedding_dim).
          specEmbB (Tensor):
              Matrix containing spectral embedding vectors from eigenvalue decomposition (N x embedding_dim).
  
      Returns:
          dis (Tensor):
              Euclidean distance values of the two sets of spectral embedding vectors.
      """
      specEmbA, specEmbB = specEmbA.to(device), specEmbB.to(device)
      A, B = specEmbA.unsqueeze(dim=1), specEmbB.unsqueeze(dim=0)
      dis = (A - B) ** 2.0
      dis = dis.sum(dim=-1).squeeze()
      return dis
  
imports: from typing import Dict, List, Tuple
  import torch
  from torch.linalg import eigh, eigvalsh
label_desc: Use image node_id 3 for calling a global function with example usage: getEuclideanDistance(specEmbA, specEmbB, device) and returns: dis