diff --git a/model_compression_toolkit/core/keras/back2framework/keras_model_builder.py b/model_compression_toolkit/core/keras/back2framework/keras_model_builder.py
index 920b720f9..6c9ee9670 100644
--- a/model_compression_toolkit/core/keras/back2framework/keras_model_builder.py
+++ b/model_compression_toolkit/core/keras/back2framework/keras_model_builder.py
@@ -302,7 +302,11 @@ def _run_operation(self,
             # Build a functional node using its args
             if isinstance(n, FunctionalNode):
                 if n.inputs_as_list:  # If the first argument should be a list of tensors:
-                    out_tensors_of_n_float = op_func(input_tensors, *n.op_call_args, **op_call_kwargs)
+                    if isinstance(op_func, KerasQuantizationWrapper):
+                        # in wrapped nodes, the op args & kwargs are already in the KerasQuantizationWrapper.
+                        out_tensors_of_n_float = op_func(input_tensors)
+                    else:
+                        out_tensors_of_n_float = op_func(input_tensors, *n.op_call_args, **op_call_kwargs)
                 else:  # If the input tensors should not be a list but iterated:
                     out_tensors_of_n_float = op_func(*input_tensors, *n.op_call_args, **op_call_kwargs)
             else:
diff --git a/model_compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v4/tp_model.py b/model_compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v4/tp_model.py
index 424bccd17..e92c51fd7 100644
--- a/model_compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v4/tp_model.py
+++ b/model_compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v4/tp_model.py
@@ -207,7 +207,7 @@ def generate_tp_model(default_config: OpQuantizationConfig,
     const_config_input16_per_tensor = const_config.clone_and_edit(
         supported_input_activation_n_bits=(8, 16),
         default_weight_attr_config=default_config.default_weight_attr_config.clone_and_edit(
-            enable_weights_quantization=True, weights_per_channel_threshold=True,
+            enable_weights_quantization=True, weights_per_channel_threshold=False,
             weights_quantization_method=tp.QuantizationMethod.POWER_OF_TWO)
     )
     const_config_input16_output16_per_tensor = const_config_input16_per_tensor.clone_and_edit(