Update inference_cost.py

Author: Harsh9650

src/qonnx/util/inference_cost.py

@@ -44,7 +44,6 @@
 from qonnx.transformation.infer_datatypes import InferDataTypes
 from qonnx.transformation.infer_shapes import InferShapes
 
-
 def compute_bops_and_macs(inf_cost_dict):
     total_bops = 0.0
     total_macs = 0.0
@@ -57,7 +56,6 @@ def compute_bops_and_macs(inf_cost_dict):
             total_macs += v
     return total_bops, total_macs
 
-
 def compute_mem_bits_and_elems(inf_cost_dict, filter_string="mem_w"):
     total_mem_bits = 0.0
     total_mem_elems = 0.0
@@ -98,6 +96,7 @@ def inference_cost(
         datatype inference and constant folding. Strongly recommended.
     :param discount_sparsity: If set, will discount op cost of MAC ops with a
         constant zero weight, and the mem cost of constant zero weights."""
+    
     combined_results = {}
     if isinstance(model_filename_or_wrapper, ModelWrapper):
         model = model_filename_or_wrapper
@@ -118,7 +117,8 @@ def inference_cost(
     model = model.transform(GiveReadableTensorNames())
     if output_onnx is not None:
         model.save(output_onnx)
-    ret = model.analysis(lambda x: infca.inference_cost(x, discount_sparsity, cost_breakdown))
+    ret = model.analysis(lambda x: infca.inference_cost(x, discount_sparsity,
+                                                        cost_breakdown))
     for i, res in ret.items():
         if i == "total_cost":
             bops, macs = compute_bops_and_macs(res)
@@ -148,10 +148,9 @@ def inference_cost(
                 per_node_breakdown[node_name] = node_res
             combined_results[i] = per_node_breakdown
     return combined_results
-
+    
 def main():
     clize.run(inference_cost)
 
-
 if __name__ == "__main__":
     main()