code: def init_conv_weight(layer):
      """initilize conv layer weight."""
      n_filters = layer.filters
      filter_shape = (layer.kernel_size,) * get_n_dim(layer)
      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)
  
      layer.set_weights(
          (
              add_noise(weight, np.array([0, 1])),
              add_noise(bias, np.array([0, 1])),
          )
      )
  
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 10 for calling a global function with example usage: init_conv_weight(layer) without return types