code: def handle_Clustering(the_class):
      def replacement_train(self, x, index, weights=None):
          """Perform clustering on a set of vectors. The index is used for assignment.
  
          Parameters
          ----------
          x : array_like
              Training vectors, shape (n, self.d). `dtype` must be float32.
          index : faiss.Index
              Index used for assignment. The dimension of the index should be `self.d`.
          weights : array_like, optional
              Per training sample weight (size n) used when computing the weighted
              average to obtain the centroid (default is 1 for all training vectors).
          """
          n, d = x.shape
          x = np.ascontiguousarray(x, dtype="float32")
          assert d == self.d
          if weights is not None:
              weights = np.ascontiguousarray(weights, dtype="float32")
              assert weights.shape == (n,)
              self.train_c(n, swig_ptr(x), index, swig_ptr(weights))
          else:
              self.train_c(n, swig_ptr(x), index)
  
      def replacement_train_encoded(
          self, x, codec, index, weights=None
      ):
          """Perform clustering on a set of compressed vectors. The index is used for assignment.
          The decompression is performed on-the-fly.
  
          Parameters
          ----------
          x : array_like
              Training vectors, shape (n, codec.code_size()). `dtype` must be `uint8`.
          codec : faiss.Index
              Index used to decode the vectors. Should have dimension `self.d`.
          index : faiss.Index
              Index used for assignment. The dimension of the index should be `self.d`.
          weigths : array_like, optional
              Per training sample weight (size n) used when computing the weighted
              average to obtain the centroid (default is 1 for all training vectors).
          """
          n, d = x.shape
          x = _check_dtype_uint8(x)
          assert d == codec.sa_code_size()
          assert codec.d == index.d
          if weights is not None:
              weights = np.ascontiguousarray(weights, dtype="float32")
              assert weights.shape == (n,)
              self.train_encoded_c(
                  n, swig_ptr(x), codec, index, swig_ptr(weights)
              )
          else:
              self.train_encoded_c(n, swig_ptr(x), codec, index)
  
      replace_method(the_class, "train", replacement_train)
      replace_method(
          the_class, "train_encoded", replacement_train_encoded
      )
  
imports: import inspect
  import faiss
  import numpy
  from faiss.loader import DirectMap, IDSelector, IDSelectorArray, IDSelectorBatch, OperatingPoints, RangeSearchResult, rev_swig_ptr, swig_ptr, try_extract_index_ivf
label_desc: Use image node_id 3 for calling a global function with example usage: handle_Clustering(the_class) without return types