code: def prepare_parameters(nntxt_str):
      proto = proto_from_str(nntxt_str)
      proto = NnpExpander(proto).execute()
      rng = np.random.RandomState(0)
      for n in proto.network:
          for v in n.variable:
              shape = tuple(
                  [d if d >= 1 else n.batch_size for d in v.shape.dim]
              )
              variable = _create_variable(v, v.name, shape, rng)
  
imports: import io
  import os
  import tempfile
  import warnings
  import numpy
  import nnabla
  import numpy
  from shutil import rmtree
  from contextlib import contextmanager
  from nnabla.utils import nnabla_pb2
  from nnabla.utils.image_utils import imsave
  import google.protobuf.text_format
  from nnabla.utils.nnp_format import nnp_version
  from nnabla.parameter import get_parameter_or_create
  from nnabla.utils.get_file_handle import get_file_handle_save
  from nnabla.utils.converter.nnabla import NnpExpander
  from nnabla.initializer import NormalInitializer, UniformInitializer, ConstantInitializer, RangeInitializer, calc_normal_std_he_forward, calc_normal_std_he_backward, calc_normal_std_glorot, calc_uniform_lim_glorot
label_desc: Use image node_id 6 for calling a global function with example usage: prepare_parameters(nntxt_str) without return types