diff --git a/tutorials/autotvm/tune_relay_x86.py b/tutorials/autotvm/tune_relay_x86.py
index 22a31b79bd0d..93a073170388 100644
--- a/tutorials/autotvm/tune_relay_x86.py
+++ b/tutorials/autotvm/tune_relay_x86.py
@@ -37,11 +37,13 @@
 # Define network
 # --------------
 # First we need to define the network in relay frontend API.
-# We can load some pre-defined network from :code:`relay.testing`.
+# We can either load some pre-defined network from :code:`relay.testing`
+# or building :any:`relay.testing.resnet` with relay.
 # We can also load models from MXNet, ONNX and TensorFlow.
 #
 # In this tutorial, we choose resnet-18 as tuning example.
 
+
 def get_network(name, batch_size):
     """Get the symbol definition and random weight of a network"""
     input_shape = (batch_size, 3, 224, 224)
@@ -73,6 +75,7 @@ def get_network(name, batch_size):
 
     return mod, params, input_shape, output_shape
 
+
 # Replace "llvm" with the correct target of your CPU.
 # For example, for AWS EC2 c5 instance with Intel Xeon
 # Platinum 8000 series, the target should be "llvm -mcpu=skylake-avx512".
@@ -121,6 +124,7 @@ def get_network(name, batch_size):
     ),
 }
 
+
 # You can skip the implementation of this function for this tutorial.
 def tune_kernels(tasks,
                  measure_option,
@@ -165,6 +169,7 @@ def tune_kernels(tasks,
                            autotvm.callback.progress_bar(n_trial, prefix=prefix),
                            autotvm.callback.log_to_file(log_filename)])
 
+
 # Use graph tuner to achieve graph level optimal schedules
 # Set use_DP=False if it takes too long to finish.
 def tune_graph(graph, dshape, records, opt_sch_file, use_DP=True):