code: def test_reshape_transpose_6d():
      class Reshape2D(gluon.HybridBlock):
          def __init__(self, factor):
              super(Reshape2D, self).__init__()
              self._factors = (int(factor),) * 2
  
          def forward(self, x):
              f1, f2 = self._factors
              N = 1
              C = 2
              H = W = 596
              # (N, f1*f2*C, H, W)
              x = mx.np.reshape(
                  x, (N, C, f1 * f2, H, W)
              )  # (N, C, f1*f2, H, W)
              x = mx.np.reshape(
                  x, (N, C, f1, f2, H, W)
              )  # (N, C, f1, f2, H, W)
              x = mx.np.transpose(
                  x, (0, 1, 4, 2, 5, 3)
              )  # (N, C, H, f1, W, f2)
              x = mx.np.reshape(
                  x, (N, C, H * f1, W * f2)
              )  # (N, C, H*f1, W*f2)
              return x
  
      class Net(gluon.HybridBlock):
          def __init__(self, **kwargs):
              super(Net, self).__init__(**kwargs)
              self.conv1 = nn.Conv2D(8, kernel_size=5)
              self.reshape2D = Reshape2D(2)
  
          def forward(self, x):
              x = self.conv1(x)
              x = self.reshape2D(x)
              return x
  
      net = Net()
      net.initialize(mx.init.Xavier(), ctx=mx.cpu())
      net.hybridize()
      data = mx.np.random.normal(size=(1, 3, 600, 600))
      output = net(data)
      a = output.asnumpy()
  
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 22 for calling a global function with example usage: test_reshape_transpose_6d() and returns: x, x