code: def test_symbolic_basic_slicing():
      def random_slice_index(shape):
          index = []
          step_switch = random.randint(-1, 1)
          for i in range(len(shape)):
              if shape[i] == 0:
                  index.append(slice(None))
                  continue
              r = random.randint(0, 5)
              if r < 2:
                  index.append(
                      random.randint(1 - shape[i], shape[i] - 1)
                  )
                  continue
              elif r < 3:
                  index.append(slice(None))
                  continue
              s = random.randint(0, shape[i] - 1)
              e = random.randint(s + 1, shape[i])
              if step_switch == 1:
                  index.append(slice(s, e, 1))
              elif step_switch == -1:
                  e -= 1
                  s -= 1
                  index.append(slice(e, s, -1))
              else:
                  index.append(slice(s, e, 2))
          return tuple(index)
  
      shapes = [
          (4, 6, 8, 5),
          (1, 1, 1, 6),
          (5, 6, 4),
          (5, 6),
          (10,),
          (100, 0, 10, 0, 0),
          (100, 0, 0),
          (0, 0, 0),
          (),
      ]
      for shape in shapes:
          cache = set()
          x = mx.np.random.normal(0, 1, shape)
          y = mx.np.random.normal(0, 1, shape)
          for _ in range(200):
              index1 = random_slice_index(shape)
              index2 = random_slice_index(x.asnumpy()[index1].shape)
              if (
                  hashable_index(index1),
                  hashable_index(index2),
              ) in cache:
                  continue
              cache.add(
                  (hashable_index(index1), hashable_index(index2))
              )
  
              # Test basic slicing on a single symbol
              class TestSlicingSingleSymbol1(gluon.HybridBlock):
                  def forward(self, x, y):
                      return x[()][index1] + y[()][index1]
  
              # Test basic slicing on a single symbol
              class TestSlicingSingleSymbol2(gluon.HybridBlock):
                  def forward(self, x, y):
                      return (x[()][index1] + y[()][index1])[index2]
  
              check_gluon_hybridize_consistency(
                  TestSlicingSingleSymbol1,
                  [x, y],
                  numpy_func=lambda a, b: a[()][index1] + b[()][index1],
              )
              check_gluon_hybridize_consistency(
                  TestSlicingSingleSymbol2,
                  [x, y],
                  numpy_func=lambda a, b: (
                      a[()][index1] + b[()][index1]
                  )[index2],
              )
  
          # Test for split/hsplit/vsplit
          class TestSlicingWithSplit(gluon.HybridBlock):
              def forward(self, x):
                  x = mx.np.split(x, shape[2], axis=2)
                  x = x[1:-1]
                  x = mx.np.concatenate(x, axis=2)
                  return x
  
          class TestSlicingWithSplit2(gluon.HybridBlock):
              def __init__(self):
                  super(TestSlicingWithSplit2, self).__init__()
                  self.layer = gluon.nn.Dense(16, flatten=False)
  
              def forward(self, x, y):
                  x = mx.np.split(x, 1)
                  x = x[0]
                  return self.layer(x[:, -1, :] + y[:, -1, :])
  
          class TestSlicingWithHSplit(gluon.HybridBlock):
              def forward(self, x):
                  x = mx.np.hsplit(x, shape[1])
                  x = x[1:-1]
                  x = mx.np.concatenate(x, axis=1)
                  return x
  
          class TestSlicingWithVSplit(gluon.HybridBlock):
              def forward(self, x):
                  x = mx.np.vsplit(x, shape[0])
                  x = x[1:-1]
                  x = mx.np.concatenate(x, axis=0)
                  return x
  
          if len(shape) > 2 and shape[2] > 2:
              check_gluon_hybridize_consistency(
                  TestSlicingWithSplit,
                  [x],
                  numpy_func=lambda a: _np.concatenate(
                      _np.split(a, shape[2], axis=2)[1:-1], axis=2
                  ),
              )
          if (
              len(shape) == 3
              and shape[0] > 0
              and shape[1] > 0
              and shape[2] > 0
          ):
              check_gluon_hybridize_consistency(
                  TestSlicingWithSplit2, [x, y]
              )
  
          if len(shape) > 1 and shape[1] > 2:
              check_gluon_hybridize_consistency(
                  TestSlicingWithHSplit,
                  [x],
                  numpy_func=lambda a: _np.concatenate(
                      _np.hsplit(a, shape[1])[1:-1], axis=1
                  ),
              )
          if len(shape) > 1 and shape[0] > 2:
              check_gluon_hybridize_consistency(
                  TestSlicingWithVSplit,
                  [x],
                  numpy_func=lambda a: _np.concatenate(
                      _np.vsplit(a, shape[0])[1:-1], axis=0
                  ),
              )
  
      for data_shape, idx in [((4, 3), 2), ((3,), -1), ((3,), 0)]:
  
          class IntegerIndexing(gluon.HybridBlock):
              def forward(self, x):
                  return x[idx]
  
          check_gluon_hybridize_consistency(
              IntegerIndexing,
              [mx.np.ones(data_shape)],
              numpy_func=lambda a: a[idx],
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  import os
  from uuid import uuid4
  import numpy
  import mxnet
  from mxnet import gluon, autograd, np, npx
  from mxnet.test_utils import use_np, assert_almost_equal, check_gluon_hybridize_consistency, assert_allclose
  from mxnet.gluon import nn
  from mxnet.base import MXNetError
  import random
  import pytest
label_desc: Use image node_id 9 for calling a global function with example usage: test_symbolic_basic_slicing() and returns: tuple, x, self, x, x, x, unknown, unknown