diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py
index fb8a08c..88d58a3 100644
--- a/ethosu/vela/tflite_graph_optimiser.py
+++ b/ethosu/vela/tflite_graph_optimiser.py
@@ -512,7 +512,10 @@
                 )
             else:
                 padding, skirt = calc_padding_and_skirt(
-                    op.attrs["padding"], op.kernel, input_shape, op.attrs.get("explicit_padding"),
+                    op.attrs["padding"],
+                    op.kernel,
+                    input_shape,
+                    op.attrs.get("explicit_padding"),
                 )
 
             op.attrs["explicit_padding"] = padding
@@ -642,11 +645,11 @@
 def convert_mul_max_to_abs_or_lrelu(op, arch, nng):
     r"""Whenever there is a subgraph with this topology:
 
-       Input    X   For X = -1 or X > 0
-       |   \   /    This subgraph can be replaced with either
-       |    Mul     an Abs (if X = -1) or a LeakyReLU (if X > 0)
-       |   /
-       Max
+    Input    X   For X = -1 or X > 0
+    |   \   /    This subgraph can be replaced with either
+    |    Mul     an Abs (if X = -1) or a LeakyReLU (if X > 0)
+    |   /
+    Max
     """
 
     if op.type == Op.Maximum:
@@ -1246,7 +1249,12 @@
                     quant = QuantizationParameters()
                     quant.zero_point = 0
                     bias_term_tens = create_const_tensor(
-                        op.name + "_bias", [1, 1, 1, 1], DataType.int16, [bias_term], np.int16, quantization=quant,
+                        op.name + "_bias",
+                        [1, 1, 1, 1],
+                        DataType.int16,
+                        [bias_term],
+                        np.int16,
+                        quantization=quant,
                     )
                     add_op.add_input_tensor(bias_term_tens)
                     add_op.set_output_tensor(op.ofm)
@@ -1370,7 +1378,12 @@
             bias_shape = [shape[-1]]
             op.set_input_tensor(
                 create_const_tensor(
-                    "bias", bias_shape, inp.dtype, np.ones(bias_shape) * bias, value_dtype=np.int32, quantization=None,
+                    "bias",
+                    bias_shape,
+                    inp.dtype,
+                    np.ones(bias_shape) * bias,
+                    value_dtype=np.int32,
+                    quantization=None,
                 ),
                 2,
             )
@@ -1392,7 +1405,12 @@
 
     for idx, sg in enumerate(nng.subgraphs):
         nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
-            nng, sg, arch, [], pre_process_list, rewrite_unsupported=False,
+            nng,
+            sg,
+            arch,
+            [],
+            pre_process_list,
+            rewrite_unsupported=False,
         )
 
     # Handle Concat Ops
@@ -1413,13 +1431,23 @@
 
     for idx, sg in enumerate(nng.subgraphs):
         nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
-            nng, sg, arch, [rewrite_split_ops], [], rewrite_unsupported=False,
+            nng,
+            sg,
+            arch,
+            [rewrite_split_ops],
+            [],
+            rewrite_unsupported=False,
         )
 
     # Handle sg input output
     for idx, sg in enumerate(nng.subgraphs):
         nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
-            nng, sg, arch, [], [fix_sg_input_output], rewrite_unsupported=False,
+            nng,
+            sg,
+            arch,
+            [],
+            [fix_sg_input_output],
+            rewrite_unsupported=False,
         )
 
     # Removal of memory only operators
@@ -1452,7 +1480,12 @@
 
     for idx, sg in enumerate(nng.subgraphs):
         nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
-            nng, sg, arch, [], op_rewrite_list, rewrite_unsupported=False,
+            nng,
+            sg,
+            arch,
+            [],
+            op_rewrite_list,
+            rewrite_unsupported=False,
         )
 
     for idx, sg in enumerate(nng.subgraphs):