code: def test_softmax_with_large_inputs():
      def softmax_forward(input_data, true_output):
          data = mx.sym.Variable("data")
          out1 = data.softmax(axis=1)
          exec1 = out1._bind(mx.cpu(), args={"data": input_data})
          exec1.forward()[0].wait_to_read()
          ndarr = exec1.outputs[0][0][0][0]
          nparr = ndarr.asnumpy()
          assert_almost_equal(nparr, true_output, rtol=1e-5, atol=1e-5)
  
      softmax_forward(
          mx.nd.array([[[[-1e30, -1e30]]]]), np.array([1.0, 1.0])
      )
      softmax_forward(
          mx.nd.array([[[[1e30, 1e30]]]]), np.array([1.0, 1.0])
      )
      softmax_forward(
          mx.nd.array([[[[-3.4e38, -3.4e38]]]]), np.array([1.0, 1.0])
      )
      softmax_forward(
          mx.nd.array([[[[3.4e38, 3.4e38]]]]), np.array([1.0, 1.0])
      )
  
imports: import sys
  import os
  import numpy
  import mxnet
  import pytest
  from mxnet.test_utils import rand_ndarray, assert_almost_equal
  from mxnet import gluon, context, use_np
  from mxnet.gluon import nn
  from mxnet.test_utils import *
  import itertools
label_desc: Use image node_id 19 for calling a global function with example usage: test_softmax_with_large_inputs() without return types