how dialated convolution actually work ?

[SlowMonk]

can someone tell me how this result came out? and if change to dialated to 2 ? what will happen??

conv = layers.Conv1D(1, 3, padding='causal', dilation_rate=1, bias_initializer=tf.keras.initializers.zeros)

np.squeeze(sequence.numpy()) ->array([0. , 0.46517092, 0.82355802, 0.99289061, 0.93429699])

np.squeeze(conv.trainable_variables[0].numpy()) ->array([-0.19279779, 0.55129746, -0.52658171])

output = conv(sequence) np.squeeze(output.numpy()) ->array([ 0. , -0.2449505 , -0.17722305, -0.15849652, -0.1033858 ])