code: def test_flatten_slice_after_conv():
      data = mx.symbol.Variable("data")
      weight = mx.symbol.Variable("weight")
      bias = mx.symbol.Variable("bias")
      conv1 = mx.symbol.Convolution(
          data=data,
          weight=weight,
          bias=bias,
          name="conv1",
          num_filter=64,
          kernel=(3, 3),
          stride=(1, 1),
      )
      flatten1 = mx.symbol.flatten(data=conv1)
      slice1 = mx.symbol.slice(data=flatten1, begin=0, end=1)
  
      shape = (2, 16, 16, 16)
      val = np.random.rand(2, 16, 16, 16).astype(np.float32)
      exe = slice1._simple_bind(context.current_context(), data=shape)
      exe.arg_arrays[0][:] = val
      exe.arg_arrays[1][:] = np.random.normal(
          size=exe.arg_arrays[1].shape
      )
      exe.arg_arrays[2][:] = np.random.normal(
          size=exe.arg_arrays[2].shape
      )
      p = exe.forward(is_train=False)
      p[0].wait_to_read()
      print(p[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 7 for calling a global function with example usage: test_flatten_slice_after_conv() without return types