diff --git a/nets/deep_sort/residual_net.py b/nets/deep_sort/residual_net.py
index 8afa1c67..750d3e47 100644
--- a/nets/deep_sort/residual_net.py
+++ b/nets/deep_sort/residual_net.py
@@ -10,9 +10,10 @@ def _batch_norm_fn(x, scope=None):
 
 
 def create_link(
-        incoming, network_builder, scope, nonlinearity=tf.nn.elu,
+        incoming, scope, nonlinearity=tf.nn.elu,
         weights_initializer=tf.truncated_normal_initializer(stddev=1e-3),
-        regularizer=None, is_first=False, summarize_activations=True):
+        i_block_bias_initializer, i_block_increase_dim, regularizer=None, 
+        is_first=False, summarize_activations=True):
     if is_first:
         network = incoming
     else:
@@ -22,8 +23,9 @@ def create_link(
             tf.summary.histogram(scope+"/activations", network)
 
     pre_block_network = incoming
-    post_block_network = network_builder(network, scope)
-
+    post_block_network = create_inner_block(
+                                        network, scope, nonlinearity, weights_initializer, i_block_bias_initializer,
+                                        regularizer, i_block_increase_dim, summarize_activations)
     incoming_dim = pre_block_network.get_shape().as_list()[-1]
     outgoing_dim = post_block_network.get_shape().as_list()[-1]
     if incoming_dim != outgoing_dim:
@@ -74,11 +76,7 @@ def residual_block(incoming, scope, nonlinearity=tf.nn.elu,
                    increase_dim=False, is_first=False,
                    summarize_activations=True):
 
-    def network_builder(x, s):
-        return create_inner_block(
-            x, s, nonlinearity, weights_initializer, bias_initializer,
-            regularizer, increase_dim, summarize_activations)
-
     return create_link(
-        incoming, network_builder, scope, nonlinearity, weights_initializer,
+        incoming, scope, nonlinearity, weights_initializer,
+        bias_initializer, increase_dim,
         regularizer, is_first, summarize_activations)