code: def compute_causalconv1d(
      x: np.ndarray, kernels: np.ndarray, dilation: int
  ) -> np.ndarray:
      """
      Naive way to compute the 1-d causal convolution
  
      Parameters:
      x: np.array
        input array
      kernels: np.array
        array of weights, [w_1, ..., w_n] where n is the kernel_size
      dilation: int
        dilation rate d > 0, when d = 1, it reduces to the regular convolution
      Returns:
        Causal 1d convolution between x and kernels with the given dilation rate
      """
  
      conv_x = np.zeros_like(x)
      # compute in a naive way.
      for t, xt in enumerate(x):
          dial_offset = 0
          for i in reversed(range(len(kernels))):
              xt_lag = (
                  x[t - dial_offset] if t - dial_offset >= 0 else 0.0
              )
              dial_offset += dilation
              conv_x[t] += kernels[i] * xt_lag
  
      return conv_x
  
imports: import mxnet
  import numpy
  from mxnet import nd
  from gluonts.mx.block.cnn import CausalConv1D
label_desc: Use image node_id 1 for calling a global function with example usage: compute_causalconv1d(x, kernels, dilation) and returns: conv_x