diff --git a/text_to_video/wan-2.2-t2v-a14b/run_mlperf.py b/text_to_video/wan-2.2-t2v-a14b/run_mlperf.py
index 147624b340..ab73c25966 100644
--- a/text_to_video/wan-2.2-t2v-a14b/run_mlperf.py
+++ b/text_to_video/wan-2.2-t2v-a14b/run_mlperf.py
@@ -46,7 +46,8 @@ def load_prompts(dataset_path):
 
 
 class Model:
-    def __init__(self, model_path, device, config, prompts, fixed_latent=None, rank=0):
+    def __init__(self, model_path, device, config,
+                 prompts, fixed_latent=None, rank=0):
         self.device = device
         self.rank = rank
         self.height = config["height"]
@@ -106,7 +107,8 @@ def flush_queries(self):
 
 
 class DebugModel:
-    def __init__(self, model_path, device, config, prompts, fixed_latent=None, rank=0):
+    def __init__(self, model_path, device, config,
+                 prompts, fixed_latent=None, rank=0):
         self.prompts = prompts
 
     def issue_queries(self, query_samples):
@@ -186,7 +188,8 @@ def get_args():
     parser.add_argument(
         "--scenario",
         default="SingleStream",
-        help="mlperf benchmark scenario, one of " + str(list(SCENARIO_MAP.keys())),
+        help="mlperf benchmark scenario, one of " +
+        str(list(SCENARIO_MAP.keys())),
     )
     parser.add_argument(
         "--user_conf",
@@ -202,7 +205,10 @@ def get_args():
         help="performance sample count",
         default=5000,
     )
-    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
+    parser.add_argument(
+        "--accuracy",
+        action="store_true",
+        help="enable accuracy pass")
     # Dont overwrite these for official submission
     parser.add_argument("--count", type=int, help="dataset items to use")
     parser.add_argument("--time", type=int, help="time to scan in seconds")
@@ -271,7 +277,10 @@ def run_mlperf(args, config):
 
         audit_config = os.path.abspath(args.audit_conf)
         if os.path.exists(audit_config):
-            settings.FromConfig(audit_config, "wan-2.2-t2v-a14b", args.scenario)
+            settings.FromConfig(
+                audit_config,
+                "wan-2.2-t2v-a14b",
+                args.scenario)
         settings.scenario = SCENARIO_MAP[args.scenario]
 
         settings.mode = lg.TestMode.PerformanceOnly
@@ -297,8 +306,10 @@ def run_mlperf(args, config):
         if args.samples_per_query:
             settings.multi_stream_samples_per_query = args.samples_per_query
         if args.max_latency:
-            settings.server_target_latency_ns = int(args.max_latency * NANO_SEC)
-            settings.multi_stream_expected_latency_ns = int(args.max_latency * NANO_SEC)
+            settings.server_target_latency_ns = int(
+                args.max_latency * NANO_SEC)
+            settings.multi_stream_expected_latency_ns = int(
+                args.max_latency * NANO_SEC)
 
         performance_sample_count = (
             args.performance_sample_count
@@ -311,7 +322,8 @@ def run_mlperf(args, config):
             count, performance_sample_count, load_query_samples, unload_query_samples
         )
 
-        lg.StartTestWithLogSettings(sut, qsl, settings, log_settings, audit_config)
+        lg.StartTestWithLogSettings(
+            sut, qsl, settings, log_settings, audit_config)
 
         lg.DestroyQSL(qsl)
         lg.DestroySUT(sut)
diff --git a/vision/classification_and_detection/tools/calibrate_torchvision_model.py b/vision/classification_and_detection/tools/calibrate_torchvision_model.py
index 3b002003ab..875d26c388 100644
--- a/vision/classification_and_detection/tools/calibrate_torchvision_model.py
+++ b/vision/classification_and_detection/tools/calibrate_torchvision_model.py
@@ -7,7 +7,7 @@
 from torch.utils.data import DataLoader, Dataset
 
 import torchvision.transforms as transforms
-from torchvision.models.quantization import *
+import torchvision.models.quantization as torchvision_quantization_models
 
 
 class CalibrationDataset(Dataset):
@@ -73,7 +73,11 @@ def main():
     )
     dataloader = DataLoader(dataset, batch_size=1)
 
-    model = eval(args.model)(pretrained=True, progress=True, quantize=False)
+    if not hasattr(torchvision_quantization_models, args.model):
+        raise ValueError(f"Model {args.model} not found in torchvision quantization models")
+    
+
+    model = getattr(torchvision_quantization_models, args.model)(pretrained=True, progress=True, quantize=False)
     quantize_model(model, dataloader)
     print(model)