[Misc] Fix linter issues in examples/fp8/quantizer/quantize.py (#3864)

examples/fp8/quantizer/quantize.py

-# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # noqa: E501
 # SPDX-License-Identifier: Apache-2.0
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -131,7 +131,8 @@ def get_tokenizer(ckpt_path, max_seq_len=MAX_SEQ_LEN, model_type=None):
         tokenizer.pad_token = tokenizer.eos_token
     if tokenizer.pad_token is None:
         tokenizer.pad_token = tokenizer.eos_token
-    assert tokenizer.pad_token is not None, f"Pad token for {model_type} cannot be set!"
+    assert (tokenizer.pad_token
+            is not None), f"Pad token for {model_type} cannot be set!"
 
     return tokenizer
 
@@ -158,9 +159,9 @@ def get_model(ckpt_path, dtype="fp16", device="cuda"):
 
     model_dtype = next(model.parameters()).dtype
     if dtype != model_dtype:
-        print(
-            f"[TensorRT-LLM][WARNING] The manually set model data type is {dtype}, "
-            f"but the data type of the HuggingFace model is {model_dtype}.")
+        print("[TensorRT-LLM][WARNING] The manually set model data type is "
+              f"{dtype}, but the data type of the HuggingFace model is "
+              f"{model_dtype}.")
 
     return model
 
@@ -244,15 +245,13 @@ def main(args):
     else:
         if "awq" in args.qformat:
             if args.calib_size > 32:
-                print(
-                    f"AWQ calibration could take longer with calib_size = {args.calib_size}, Using"
-                    " calib_size=32 instead")
+                print("AWQ calibration could take longer with calib_size = "
+                      f"{args.calib_size}, Using calib_size=32 instead")
                 args.calib_size = 32
-            print(
-                "\nAWQ calibration could take longer than other calibration methods. Please"
-                " increase the batch size to speed up the calibration process. Batch size can be"
-                " set by adding the argument --batch_size <batch_size> to the command line.\n"
-            )
+            print("\nAWQ calibration could take longer than other calibration "
+                  "methods. Please increase the batch size to speed up the "
+                  "calibration process. Batch size can be set by adding the "
+                  "argument --batch_size <batch_size> to the command line.\n")
 
         calib_dataloader = get_calib_dataloader(
             tokenizer=tokenizer,
@@ -287,9 +286,8 @@ def main(args):
 
     with torch.inference_mode():
         if model_type is None:
-            print(
-                f"Unknown model type {type(model).__name__}. Continue exporting..."
-            )
+            print(f"Unknown model type {type(model).__name__}. Continue "
+                  "exporting...")
             model_type = f"unknown:{type(model).__name__}"
 
         export_path = args.output_dir