original_docstring: 
      compute the eucilidean distance matrix between embeddings1 and embeddings2
      using cpu
      
docstring: compute the eucilidean distance matrix between embeddings1 and embeddings2
  using cpu
docstring_tokens: compute the eucilidean distance matrix between embeddings1 and embeddings2 using cpu
code: def pdist_np(emb1, emb2):
      '''
      compute the eucilidean distance matrix between embeddings1 and embeddings2
      using cpu
      '''
      m, n = emb1.shape[0], emb2.shape[0]
      emb1_pow = np.square(emb1).sum(axis = 1)[..., np.newaxis]
      emb2_pow = np.square(emb2).sum(axis = 1)[np.newaxis, ...]
      dist_mtx = -2 * np.matmul(emb1, emb2.T) + emb1_pow + emb2_pow
      # dist_mtx = np.sqrt(dist_mtx.clip(min = 1e-12))
      return dist_mtx
code_tokens: def pdist_np ( emb1 , emb2 ) : m , n = emb1 . shape [ 0 ] , emb2 . shape [ 0 ] emb1_pow = np . square ( emb1 ) . sum ( axis = 1 ) [ ... , np . newaxis ] emb2_pow = np . square ( emb2 ) . sum ( axis = 1 ) [ np . newaxis , ... ] dist_mtx = - 2 * np . matmul ( emb1 , emb2 . T ) + emb1_pow + emb2_pow return dist_mtx
short_docstring: compute the eucilidean distance matrix between embeddings1 and embeddings2
  using cpu
short_docstring_tokens: compute the eucilidean distance matrix between embeddings1 and embeddings2 using cpu
comment: '''
      compute the eucilidean distance matrix between embeddings1 and embeddings2
      using cpu
      '''$SEP$# dist_mtx = np.sqrt(dist_mtx.clip(min = 1e-12))