code: def concatenate_over(argname):
      """Decorator to "vectorize" functions and concatenate outputs"""
  
      def _prepare_args(arg_map, value):
          params = copy(arg_map)
          params[argname] = value
          return params
  
      @decorator
      def _concatenate_over(func, *args, **kwargs):
          """Validate that an agument is a proportion [0, 1.0]"""
          arg_map = map_parameters_in_fn_call(args, kwargs, func)
          value = arg_map.get(argname)
  
          if isinstance(value, list):
              return list(
                  itertools.chain.from_iterable(
                      func(**_prepare_args(arg_map, v)) for v in value
                  )
              )
          else:
              return func(**arg_map)
  
      return _concatenate_over
  
imports: from copy import copy, deepcopy
  import abc
  import inspect
  import importlib
  import itertools
  from collections.abc import Mapping
  import pandas
  from decorator import decorator
  from sklearn_evaluation.exceptions import DataSelectorError
  from sklearn_evaluation.util import map_parameters_in_fn_call
  from sklearn_evaluation.table import Table
label_desc: Use image node_id 4 for calling a global function with example usage: concatenate_over(argname) and returns: _concatenate_over, params, list, func