Address some linting issues

Author: iksnagreb

src/qonnx/transformation/general.py

@@ -405,7 +405,6 @@ def apply(self, model):  # noqa
                 # Length of sorted and original input list must match
                 assert len(inputs) == len(node.input)
                 # Reassigned inputs from sorted categories
-                #   Note: ONNX does not allow direct assignment to node.input
                 for i, name in enumerate(inputs):
                     # The graph has been modified if any input is reordered
                     if node.input[i] != name:

tests/transformation/test_sort_commutative_inputs_initializer_last.py

@@ -1,26 +1,34 @@
 # Set pytest parameters
 import pytest
+
 # Numpy for handling simulation of tensor operations
 import numpy as np
+
 # Helper for creating ONNX nodes
 from onnx import TensorProto
 from onnx import helper as oh
+
 # QONNX wrapper of ONNX model graphs
 from qonnx.core.modelwrapper import ModelWrapper
-# QONNX utility  for creating models from ONNX graphs
-from qonnx.util.basic import qonnx_make_model
+
 # Execute QONNX model graphs
 from qonnx.core.onnx_exec import execute_onnx
+
 # Graph transformation to be tested: Sorts the input list of commutative
 # operations to have all dynamic inputs first followed by all initializer inputs
 from qonnx.transformation.general import SortCommutativeInputsInitializerLast
 
+# QONNX utility  for creating models from ONNX graphs
+from qonnx.util.basic import qonnx_make_model
+
 
 # Specify how many inputs the test should cover
 @pytest.mark.parametrize("num_inputs", [4, 5, 6])
 # Specify which inputs should be turned into initializers
 @pytest.mark.parametrize(
+    # fmt: off
     "initializers", [[], [0], [1], [0, 1], [0, 3], [0, 1, 2, 3]]
+    # fmt: on
 )
 # Tests the SortCommutativeInputsInitializerLast transformation
 def test_sort_commutative_inputs_initializer_last(num_inputs, initializers):
@@ -29,11 +37,15 @@ def test_sort_commutative_inputs_initializer_last(num_inputs, initializers):
     # We will use the Sum ONNX operation to test this behavior, as it allows for
     # arbitrary many inputs
     node = oh.make_node(
+        # fmt: off
         op_type="Sum", inputs=inputs, outputs=["out"], name="Sum"
+        # fmt: on
     )
     # Create value infos for all input and the output tensor
     inputs = [
+        # fmt: off
         oh.make_tensor_value_info(i, TensorProto.FLOAT, (16,)) for i in inputs
+        # fmt: on
     ]
     out = oh.make_tensor_value_info("out", TensorProto.FLOAT, (16,))
     # Make a graph comprising the Sum node and value infos for all inputs and
@@ -42,9 +54,7 @@ def test_sort_commutative_inputs_initializer_last(num_inputs, initializers):
     # Wrap the graph in an QONNX model wrapper
     model = ModelWrapper(qonnx_make_model(graph, producer_name="qonnx-tests"))
     # Prepare the execution context
-    context = {
-        f"in{i}": np.random.rand(16) for i in range(num_inputs)
-    }
+    context = {f"in{i}": np.random.rand(16) for i in range(num_inputs)}
     # Make sure all inputs are of type float32
     context = {key: value.astype(np.float32) for key, value in context.items()}
     # Turn selected inputs into initializers
@@ -57,7 +67,9 @@ def test_sort_commutative_inputs_initializer_last(num_inputs, initializers):
     # Note: No cleanup, as the tested transformation is part of the cleanup, and
     # we want to test this in isolation
     model = model.transform(
+        # fmt: off
         SortCommutativeInputsInitializerLast(), cleanup=False
+        # fmt: on
     )
     # Execute the ONNX model after transforming
     out_produced = execute_onnx(model, context)["out"]
@@ -71,8 +83,9 @@ def test_sort_commutative_inputs_initializer_last(num_inputs, initializers):
             seen_initializer = True
         # If there has already been an initializer, this input must be an
         # initializer as well
-        assert not seen_initializer or model.get_initializer(i) is not None, \
-            "Non-initializer input following initializer after sorting"
+        assert (
+            not seen_initializer or model.get_initializer(i) is not None
+        ), "Non-initializer input following initializer after sorting"
 
     # Outputs before and after must match
     assert np.allclose(out_produced, out_expected)