code: def insert_miniblock(
      self, idx, miniblock_config, input_dim, noise=None, scheme=0
  ):
      assert 0 <= idx < len(self.miniblocks), (
          "Invalid miniblock index %d" % idx
      )
      if miniblock_config["bc_mode"]:
          # DenseNet-BC
          if scheme == 0:
              copy_idx = idx
              copy_miniblock = self.miniblocks[copy_idx]
              new_in_bottle = copy_miniblock.in_bottle.copy()
              new_in_layer = new_in_bottle.layers[0]
              pad_kernel_shape = list(new_in_layer.init["kernel"].shape)
              pad_kernel_shape[2] = copy_miniblock.out_features_dim
              new_in_layer.init["kernel"] = np.concatenate(
                  [
                      new_in_layer.init["kernel"],
                      np.zeros(pad_kernel_shape),
                  ],
                  axis=2,
              )
              if new_in_layer.pre_activation and new_in_layer.use_bn:
                  new_in_layer.init["beta"] = np.concatenate(
                      [
                          new_in_layer.init["beta"],
                          np.zeros([copy_miniblock.out_features_dim]),
                      ]
                  )
                  new_in_layer.init["gamma"] = np.concatenate(
                      [
                          new_in_layer.init["gamma"],
                          np.ones([copy_miniblock.out_features_dim]),
                      ]
                  )
                  new_in_layer.init["moving_mean"] = np.concatenate(
                      [
                          new_in_layer.init["moving_mean"],
                          np.zeros([copy_miniblock.out_features_dim]),
                      ]
                  )
                  new_in_layer.init["moving_variance"] = np.concatenate(
                      [
                          new_in_layer.init["moving_variance"],
                          np.ones([copy_miniblock.out_features_dim]),
                      ]
                  )
              new_in_layer.init["kernel"] = apply_noise(
                  new_in_layer.init["kernel"], noise.get("wider")
              )
              if copy_miniblock.out_bottle is None:
                  (
                      new_branches,
                      indices,
                  ) = copy_miniblock.remapped_branches(noise=noise)
                  new_miniblock = LayerMultiBranch(
                      "M_%d" % (idx + 2),
                      new_branches,
                      merge=copy_miniblock.merge,
                      in_bottle=new_in_bottle,
                  )
                  old_size = len(indices)
                  indices = np.concatenate(
                      [np.arange(old_size), indices]
                  )
                  magnifier = get_magnifier(old_size, indices)
  
                  prev_miniblock_out_dim = input_dim
                  for _i in range(0, idx):
                      prev_miniblock_out_dim += self.miniblocks[
                          _i
                      ].out_features_dim
                  indices = np.concatenate(
                      [
                          np.arange(prev_miniblock_out_dim),
                          indices + prev_miniblock_out_dim,
                      ]
                  )
                  magnifier = np.concatenate(
                      [
                          [1] * prev_miniblock_out_dim,
                          magnifier,
                      ]
                  )
                  prev_miniblock_out_dim += old_size
                  for _i in range(idx + 1, len(self.miniblocks)):
                      miniblock_out_dim = self.miniblocks[
                          _i
                      ].out_features_dim
                      self.miniblocks[_i].id = "M_%d" % (_i + 2)
                      self.miniblocks[_i].prev_widen(
                          indices, magnifier, noise=noise
                      )
                      indices = np.concatenate(
                          [
                              indices,
                              np.arange(
                                  prev_miniblock_out_dim,
                                  prev_miniblock_out_dim
                                  + miniblock_out_dim,
                              ),
                          ]
                      )
                      magnifier = np.concatenate(
                          [
                              magnifier,
                              [1] * miniblock_out_dim,
                          ]
                      )
                      prev_miniblock_out_dim += miniblock_out_dim
                  self.miniblocks = (
                      self.miniblocks[: idx + 1]
                      + [new_miniblock]
                      + self.miniblocks[idx + 1 :]
                  )
                  return indices, magnifier
              else:
                  raise NotImplementedError
          else:
              # identity scheme
              raise NotImplementedError
      else:
          # DenseNet without BC
          raise NotImplementedError
  
imports: import tensorflow
  from .basic_model import BasicModel
  from .layers import ConvLayer, FCLayer, PoolLayer, get_magnifier, apply_noise
  from ..data_providers.base_provider import DataProvider
  from .layer_cascade import LayerCascade
  from .layer_multi_branch import LayerMultiBranch
  import numpy
label_desc: Use image node_id 10 for calling the DenseBlock obj's underlying member method code with example usage: obj.insert_miniblock(idx, miniblock_config, input_dim, noise, scheme) and returns: indices,  magnifier