code: def test_causal_conv_1d() -> None:
      """
      Here we test whether the causal conv1d matches the naive computation.
      """
      x = nd.random.normal(0, 1, shape=(1, 1, 10))
  
      conv1d = CausalConv1D(
          channels=1, kernel_size=3, dilation=3, activation=None
      )
      conv1d.collect_params().initialize(
          mx.init.One(), ctx=mx.cpu(), force_reinit=True
      )
  
      y1 = conv1d(x).reshape(shape=(-1,)).asnumpy()
      y2 = compute_causalconv1d(
          x=x.reshape(shape=(-1,)).asnumpy(),
          kernels=np.asarray([1.0] * 3),
          dilation=3,
      )
  
      assert (
          np.max(np.abs(y1 - y2)) < 1e-5
      ), "1d Causal Convolution calculation incorrect!"
  
imports: import mxnet
  import numpy
  from mxnet import nd
  from gluonts.mx.block.cnn import CausalConv1D
label_desc: Use image node_id 2 for calling a global function with example usage: test_causal_conv_1d() without return types