code: def deeper_conv_block(conv_layer, kernel_size, weighted=True):
      """deeper conv layer."""
      n_dim = get_n_dim(conv_layer)
      filter_shape = (kernel_size,) * 2
      n_filters = conv_layer.filters
      weight = np.zeros((n_filters, n_filters) + filter_shape)
      center = tuple(map(lambda x: int((x - 1) / 2), filter_shape))
      for i in range(n_filters):
          filter_weight = np.zeros((n_filters,) + filter_shape)
          index = (i,) + center
          filter_weight[index] = 1
          weight[i, ...] = filter_weight
      bias = np.zeros(n_filters)
      new_conv_layer = get_conv_class(n_dim)(
          conv_layer.filters, n_filters, kernel_size=kernel_size
      )
      bn = get_batch_norm_class(n_dim)(n_filters)
  
      if weighted:
          new_conv_layer.set_weights(
              (
                  add_noise(weight, np.array([0, 1])),
                  add_noise(bias, np.array([0, 1])),
              )
          )
          new_weights = [
              add_noise(
                  np.ones(n_filters, dtype=np.float32), np.array([0, 1])
              ),
              add_noise(
                  np.zeros(n_filters, dtype=np.float32),
                  np.array([0, 1]),
              ),
              add_noise(
                  np.zeros(n_filters, dtype=np.float32),
                  np.array([0, 1]),
              ),
              add_noise(
                  np.ones(n_filters, dtype=np.float32), np.array([0, 1])
              ),
          ]
          bn.set_weights(new_weights)
  
      return [StubReLU(), new_conv_layer, bn]
  
file_path: nni/nni/algorithms/hpo/networkmorphism_tuner/layer_transformer.py
imports: import numpy
  from .layers import StubDense, StubReLU, get_batch_norm_class, get_conv_class, get_n_dim
label_desc: Use image node_id 1 for calling a global function with example usage: deeper_conv_block(conv_layer, kernel_size, weighted) and returns: list