resolved lgtm errors

Signed-off-by: Ajay Sarjoo <ajay.sarjoo@outlook.com>

art/estimators/certification/randomized_smoothing/pytorch.py

@@ -32,7 +32,6 @@
 from art.estimators.certification.randomized_smoothing.randomized_smoothing import RandomizedSmoothingMixin
 import art.estimators.certification.randomized_smoothing.smoothmix.train_smoothmix as train_smoothmix
 from art.defences.preprocessor.gaussian_augmentation import GaussianAugmentation
-from art.attacks.evasion.iterative_method import BasicIterativeMethod
 import torch
 
 if TYPE_CHECKING:

art/estimators/certification/randomized_smoothing/smoothmix/smooth_pgd_attack.py

@@ -27,13 +27,8 @@
 
 """
 
-import numpy as np
 import torch
-from torch.autograd import Variable
-from torch.nn import CrossEntropyLoss
-import torch.nn as nn
 import torch.nn.functional as F
-import torch.optim as optim
 from typing import Optional
 
 
@@ -70,7 +65,7 @@ def __init__(
 
     def attack(
         self, 
-        model: nn.Module, 
+        model: torch.nn.Module, 
         inputs: torch.Tensor, 
         labels: torch.Tensor, 
         noises: torch.Tensor = None

art/estimators/certification/randomized_smoothing/smoothmix/train_smoothmix.py

@@ -24,7 +24,7 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import logging
-from typing import List, Optional, Tuple, Union, Generator, TYPE_CHECKING
+from typing import Tuple, TYPE_CHECKING
 import time # Remove after testing
 import numpy as np
 
@@ -114,7 +114,6 @@ def fit_pytorch(self, x: np.ndarray, y: np.ndarray, batch_size: int, nb_epochs:
         batch_time = AverageMeter()  # Remove later
         data_time = AverageMeter()  # Remove later
         losses = AverageMeter()  # Remove later
-        losses_reg = AverageMeter()  # Remove later
         end = time.time()  # Remove later
 
         # Put the model in the training mode