code: def test_strided_convolution_block_1d():
      """perception test"""
      in_dict = build_input_dict(dims=[100, 1, 32])
      net: StridedConvolutionBlock = StridedConvolutionBlock(
          in_keys="in_key",
          out_keys="out_key",
          in_shapes=(1, 32),
          hidden_channels=[4],
          hidden_strides=[1],
          hidden_kernels=[8],
          non_lin=nn.ReLU,
          convolution_dimension=1,
          hidden_dilations=[2],
          hidden_padding=[0],
          padding_mode=None,
      )
      str(net)
      out_dict = net(in_dict)
      _ = net.get_num_of_parameters()
      assert isinstance(out_dict, Dict)
      assert set(net.out_keys).issubset(set(out_dict.keys()))
      assert net.output_channels == 4
      assert out_dict[net.out_keys[0]].shape[-2] == net.output_channels
      assert out_dict[net.out_keys[0]].shape == (100, 4, 18)
      assert net.out_shapes() == [out_dict[net.out_keys[0]].shape[-2:]]
  
imports: from typing import Dict
  from torch import nn
  from maze.perception.blocks.feed_forward.strided_conv import StridedConvolutionBlock
  from maze.test.perception.perception_test_utils import build_input_dict
label_desc: Use image node_id 2 for calling a global function with example usage: test_strided_convolution_block_1d() without return types