code: def get_minimal_dtype(arrays, increase_itemsize_factor=1):
      assert isinstance(
          arrays, list
      ), "Expected a list of arrays or dtypes, got type %s." % (
          type(arrays),
      )
      assert (
          len(arrays) > 0
      ), "Cannot estimate minimal dtype of an empty iterable."
  
      input_dts = normalize_dtypes(arrays)
  
      # This loop construct handles (1) list of a single dtype, (2) list of two
      # dtypes and (3) list of 3+ dtypes. Note that promote_dtypes() always
      # expects exactly two dtypes.
      promoted_dt = input_dts[0]
      input_dts = input_dts[1:]
      while len(input_dts) >= 1:
          promoted_dt = np.promote_types(promoted_dt, input_dts[0])
          input_dts = input_dts[1:]
  
      if increase_itemsize_factor > 1:
          assert isinstance(promoted_dt, np.dtype), (
              "Expected numpy.dtype output from numpy.promote_dtypes, got type "
              "%s." % (type(promoted_dt),)
          )
          return increase_itemsize_of_dtype(
              promoted_dt, increase_itemsize_factor
          )
      return promoted_dt
  
imports: from __future__ import print_function, division, absolute_import
  import numpy
  import six.moves
  import imgaug
label_desc: Use image node_id 8 for calling a global function with example usage: get_minimal_dtype(arrays, increase_itemsize_factor) and returns: promoted_dt, increase_itemsize_of_dtype