[Lint] rerun linter, fix errors

Author: maltanar

src/qonnx/core/modelwrapper.py

@@ -183,7 +183,7 @@ def transform(
         if self.fix_float64:
             (transformed_model, model_was_changed) = DoubleToSingleFloat().apply(transformed_model)
 
-        if apply_to_subgraphs and not use_preorder_traversal:
+        if apply_to_subgraphs and (use_preorder_traversal is False):
             transformed_model.transform_subgraphs(
                 transformation, make_deepcopy, cleanup, apply_to_subgraphs, use_preorder_traversal
             )

src/qonnx/transformation/fixedpt_quantize.py

@@ -41,14 +41,19 @@ def default_op_filter(op):
 
 class FixedPointQuantizeParamsFromDict(Transformation):
     """
-    Quantize model parameters to a given fixed-point representation.
-    The self.max_err dictionary stores the maximum error for each quantized input after calling.
-    Parameters:
-        fixedpt_dict: Dictionary containing tensor names and their corresponding target fixed-point
-                       data type or its canonical name
-        rounding_mode: Rounding mode used for conversion into fixed point.
-                       Default is "ROUND",
-                       possible values: ["ROUND", "HALF_EVEN", "CEIL", "FLOOR", "UP", "DOWN", "HALF_UP", "HALF_DOWN"]
+        Quantize model parameters to a given fixed-point representation.
+        The self.max_err dictionary stores the maximum error for each quantized input after calling.
+        Parameters:
+            fixedpt_dict: Dictionary containing tensor names and their corresponding target fixed-point
+    <<<<<<< HEAD
+                           data type or its canonical name
+    =======
+                          data type or its canonical name
+    >>>>>>> 7dfc4b8 ([Lint] rerun linter, fix errors)
+            rounding_mode: Rounding mode used for conversion into fixed point.
+                           Default is "ROUND",
+                           possible values: ["ROUND", "HALF_EVEN", "CEIL", "FLOOR", "UP", "DOWN",
+                           "HALF_UP", "HALF_DOWN"]
     """
 
     def __init__(self, fixedpt_dict, rounding_mode="ROUND"):
@@ -66,7 +71,7 @@ def apply(self, model: ModelWrapper):
                 if current_dtype.is_fixed_point():
                     warn(
                         f"Tensor {tname} is already a {current_dtype.get_canonical_name()} type. "
-                        f"Recasting to {tdtype.get_canonical_name()}"
+                        "Recasting to {tdtype.get_canonical_name()}"
                     )
 
                 in1_t_new = self.round_func(in1_t.astype(np.float32) / tdtype.scale_factor()) * tdtype.scale_factor()