code: def test_feed_forward_convolution_block():
      """perception test"""
      in_dict = build_input_dict(dims=[100, 3, 64, 64])
      net: VGGConvolutionBlock = VGGConvolutionBlock(
          in_keys="in_key",
          out_keys="out_key",
          in_shapes=(3, 64, 64),
          hidden_channels=[4, 8, 16],
          non_lin=nn.ReLU,
      )
      str(net)
      out_dict = net(in_dict)
  
      assert isinstance(out_dict, Dict)
      assert set(net.out_keys).issubset(set(out_dict.keys()))
      assert net.output_channels == 16
      assert out_dict[net.out_keys[0]].shape[-3] == net.output_channels
      assert out_dict[net.out_keys[0]].shape == (100, 16, 8, 8)
      assert net.out_shapes() == [out_dict[net.out_keys[0]].shape[-3:]]
  
imports: from typing import Dict
  from torch import nn
  from maze.perception.blocks.feed_forward.vgg_conv import VGGConvolutionBlock
  from maze.test.perception.perception_test_utils import build_input_dict
label_desc: Use image node_id 1 for calling a global function with example usage: test_feed_forward_convolution_block() without return types