code: def test_quantized_reshape():
      test_cases = [
          ((2, 3, 5, 5), (-2, -1), False, (2, 75)),
          ((2, 3, 5, 5), (-2, -2, -1), False, (2, 3, 25)),
          ((5, 3, 4, 5), (-2, -1, -2), False, (5, 15, 4)),
          ((2, 3, 5, 4), (-1, -2, -2), False, (8, 3, 5)),
          ((2, 3, 5, 5), (-2, -2, -2, -2), False, (2, 3, 5, 5)),
          ((2, 1, 4, 5), (-2, -3, -2, -2), False, (2, 4, 5)),
          ((1, 1, 4, 1), (-3, -3, -2, -2), False, (4, 1)),
          ((1, 1, 1, 1), (-3, -3, -3, -3), False, ()),
          ((2, 4, 5, 3), (-1, 2, 2, 1), False, (30, 2, 2, 1)),
          ((2, 3, 5, 6), (-4,), False, (2, 3, 5, 6)),
          ((2, 3, 5, 6), (6, 1, -4), False, (6, 1, 5, 6)),
          ((2, 3, 5, 6), (-5, -5), False, (6, 30)),
          ((2, 3, 5, 6), (-5, -1), False, (6, 30)),
          ((64,), (-6, 16, 4), False, (16, 4)),
          ((64,), (-6, 16, -1), False, (16, 4)),
          ((64, 1, 2, 3), (-6, 16, -1, -4), False, (16, 4, 1, 2, 3)),
          ((8, 5, 4, 6), (-4, -1, 3, -6), True, (8, 5, 4, 2, 3)),
      ]
  
      def check_quantized_reshape(
          shape, qdtype, newshape, reverse, expected_ret_shape
      ):
          data_low, data_high = get_low_high(qdtype)
          qdata = mx.np.random.uniform(
              low=data_low, high=data_high, size=shape
          ).astype(qdtype)
          min_data = mx.np.array([-1023.343], dtype="float32")
          max_data = mx.np.array([2343.324275], dtype="float32")
          qoutput, min_output, max_output = npx.quantized_reshape(
              qdata,
              min_data,
              max_data,
              newshape=newshape,
              reverse=reverse,
          )
          assert qoutput.shape == expected_ret_shape
          assert same(
              qdata.asnumpy().flatten(), qoutput.asnumpy().flatten()
          )
          assert same(min_data.asnumpy(), min_output.asnumpy())
          assert same(max_data.asnumpy(), max_output.asnumpy())
  
      for qdtype in ["int8", "uint8"]:
          for (
              shape,
              newshape,
              reverse,
              expected_ret_shape,
          ) in test_cases:
              check_quantized_reshape(
                  shape, qdtype, newshape, reverse, expected_ret_shape
              )
  
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 21 for calling a global function with example usage: test_quantized_reshape() without return types