code: def resolve_broadcast_params(inputs, function_proto, batch_size):
      """Resolve shape parameter and returns shape."""
      f = function_proto  # alias
  
      # A. Detect multiple negative dimensions (not allowed).
      negative_count = 0
      for d in f.broadcast_param.shape.dim:
          if d < 0:
              negative_count += 1
      if negative_count > 1:
          raise ValueError(
              "Reshape: shape has multiple negative number."
          )
  
      # B. Fill negative dimensions with batch size.
      shape = tuple(
          [
              d if d >= 0 else batch_size
              for d in f.broadcast_param.shape.dim
          ]
      )
      return shape
  
imports: from collections import OrderedDict
  import google.protobuf.text_format
  import itertools
  import numpy
  import os
  import re
  import shutil
  import tempfile
  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
  from nnabla.logger import logger
  from nnabla.parameter import get_parameter_or_create
  from nnabla.utils import nnabla_pb2
  from nnabla.utils.data_iterator import data_iterator_csv_dataset, data_iterator_cache
  from nnabla.utils.create_cache import CreateCache
  from nnabla.utils.load_function import _create_function_instance
  from nnabla.utils.nnp_format import nnp_version
  from nnabla.utils.communicator_util import current_communicator, single_or_rankzero
  from nnabla.utils.learning_rate_scheduler import PolynomialScheduler, CosineScheduler, ExponentialScheduler, StepScheduler, LinearWarmupScheduler
  from legacy_network import Network
  from nnabla.utils.progress import progress
  from legacy_get_file_handle import get_file_handle_load, get_buf_type
  import nnabla
  import nnabla.function
  import nnabla.solver
label_desc: Use image node_id 2 for calling a global function with example usage: resolve_broadcast_params(inputs, function_proto, batch_size) and returns: shape