code: def test_quantized_transpose():
      def check_quantized_transpose(shape, qdtype, axes):
          data_low, data_high = get_low_high(qdtype)
          data = (
              mx.np.random.uniform(
                  low=data_low, high=data_high, size=shape
              )
              .astype(qdtype)
              .astype("float32")
          )
          min_data = mx.np.array(
              [mx.np.min(data).astype("float32").item()]
          )
          max_data = mx.np.array(
              [mx.np.max(data).astype("float32").item()]
          )
          qdata = data.astype(qdtype)
          output = mx.np.transpose(data, axes=axes)
          qoutput, min_output, max_output = npx.quantized_transpose(
              qdata, min_data, max_data, axes=axes
          )
          assert_almost_equal(output.asnumpy(), qoutput.asnumpy())
          assert_almost_equal(min_output.item(), min_data.item())
          assert_almost_equal(max_output.item(), max_data.item())
  
      for qtype in ["int8", "uint8"]:
          check_quantized_transpose((), qtype, ())
          check_quantized_transpose((2, 3), qtype, (1, 0))
          check_quantized_transpose((8, 21), qtype, (1, 0))
          check_quantized_transpose((7, 3, 9), qtype, (2, 1, 0))
          check_quantized_transpose((5, 3, 6, 8), qtype, (2, 3, 0, 1))
  
imports: import os
  import mxnet
  import numpy
  from mxnet import npx
  from mxnet.util import use_np
  from mxnet.gluon.model_zoo import vision
  from mxnet.test_utils import assert_almost_equal, assert_exception, rand_ndarray, rand_shape_nd, same, DummyIter
  from common import xfail_when_nonstandard_decimal_separator
  from mxnet.io import NDArrayIter
  import unittest
  import operator
label_desc: Use image node_id 16 for calling a global function with example usage: test_quantized_transpose() without return types