
            <p>We use PyTorch&#x27;s <code  class="highlight"><span></span><span class="n">Conv1d</span></code>
 module. We set the number of groups to be equal to the number of channels so that it does a separate convolution (with different kernels) for each channel. We add padding to both sides and later crop the right most <code  class="highlight"><span></span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span></code>
 results </p>

        </div>
        <div class='co