code: def n_feature_hash(feature, dims, seeds):
      
      vec = np.zeros(sum(dims))
      offset = 0
  
      for seed, dim in zip(seeds, dims):
          vec[offset:(offset + dim)] = feature_hash(feature, dim, seed)
          offset += dim
  
      return vec
docs: N-hot-encoded feature hashing.
  
      Args:
          feature (str): Target feature represented as string.
          dims (list of int): Number of dimensions for each hash value.
          seeds (list of float): Seed of each hash function (mmh3).
  
      Returns:
          numpy 1d array: n-hot-encoded feature vector for `s`.