code: def deepen(self, loc, new_layer_config, image_channel=3):
      new_layer_config["pre_activation"] = self.net_config[
          "pre_activation"
      ]
      block_idx = loc["block"]
      if block_idx == 0:
          input_dim = image_channel
      elif isinstance(self.blocks[block_idx - 1], TransitionBlock):
          input_dim = self.blocks[block_idx - 1].out_features_dim
      else:
          input_dim = self.blocks[block_idx - 1].out_features_dim(
              self.blocks[block_idx - 2].out_features_dim
          )
  
      return self.blocks[block_idx].deepen(
          loc, new_layer_config, input_dim
      )
  
imports: import tensorflow
  from models.basic_model import BasicModel
  from models.layers import ConvLayer, FCLayer, PoolLayer, get_magnifier, apply_noise
  from data_providers.base_provider import DataProvider
  from models.layer_cascade import LayerCascade
  from models.layer_multi_branch import LayerMultiBranch
  import numpy
label_desc: Use image node_id 17 for calling the DenseNetConfig obj's underlying member method code with example usage: obj.deepen(loc, new_layer_config, image_channel) and returns: self