code: def get_bucket(self, hash_name, bucket_key):
          
          results = []
          for row in self._get_bucket_rows(hash_name, bucket_key):
              val_dict = pickle.loads(row)
              # Depending on type (sparse or not) reconstruct vector
              if 'sparse' in val_dict:
  
                  # Fill these for COO creation
                  row = []
                  col = []
                  data = []
  
                  # For each non-zero element, append values
                  for e in val_dict['nonzeros']:
                      row.append(e[0])  # Row index
                      data.append(e[1])  # Value
                      col.append(0)  # Column index (always 0)
  
                  # Create numpy arrays for COO creation
                  coo_row = numpy.array(row, dtype=numpy.int32)
                  coo_col = numpy.array(col, dtype=numpy.int32)
                  coo_data = numpy.array(data)
  
                  # Create COO sparse vector
                  vector = scipy.sparse.coo_matrix((coo_data, (coo_row, coo_col)), shape=(val_dict['dim'], 1))
  
              else:
                  vector = numpy.fromstring(val_dict['vector'],
                                            dtype=val_dict['dtype'])
  
              # Add data to result tuple, if present
              results.append((vector, val_dict.get('data')))
  
          return results
docs: Returns bucket content as list of tuples (vector, data).