@@ -1,4 +1,4 @@
 # PACKAGE IMPORTS FOR EXTERNAL USAGE
 from super_gradients.common.aws_connection.aws_connector import AWSConnector
 
-__all__ = ['AWSConnector']
+__all__ = ["AWSConnector"]

@@ -24,25 +24,23 @@ class AWSConnector:
             try:
                 if profile_name and boto3.session.Session(profile_name=profile_name).get_credentials():
                     # TRY USING A SPECIFIC PROFILE_NAME (USING A CREDENTIALS FILE)
-                    logger.info('Trying to connect to AWS using Credentials File with profile_name: ' + profile_name)
+                    logger.info("Trying to connect to AWS using Credentials File with profile_name: " + profile_name)
 
                     session = boto3.Session(profile_name=profile_name)
                     return session
 
             except ProfileNotFound as profileNotFoundException:
                 logger.debug(
-                    '[' + current_class_name + '] - Could not find profile name - Trying using Default Profile/IAM Role' + str(
-                        profileNotFoundException))
+                    "[" + current_class_name + "] - Could not find profile name - Trying using Default Profile/IAM Role" + str(profileNotFoundException)
+                )
 
             # TRY USING AN IAM ROLE (OR *DEFAULT* CREDENTIALS - USING A CREDENTIALS FILE)
-            logger.info('Trying to connect to AWS using IAM role or Default Credentials')
+            logger.info("Trying to connect to AWS using IAM role or Default Credentials")
             session = boto3.Session()
             return session
 
         except Exception as ex:
-            logger.critical(
-                '[' + current_class_name + '] - Caught Exception while trying to connect to AWS Credentials Manager ' + str(
-                    ex))
+            logger.critical("[" + current_class_name + "] - Caught Exception while trying to connect to AWS Credentials Manager " + str(ex))
             return None
 
     @staticmethod
@@ -57,7 +55,7 @@ class AWSConnector:
 
         aws_session = AWSConnector.__create_boto_3_session(profile_name=profile_name)
         if aws_session is None:
-            logger.error('Failed to initiate an AWS Session')
+            logger.error("Failed to initiate an AWS Session")
 
         return aws_session
 
@@ -74,7 +72,7 @@ class AWSConnector:
 
         aws_session = AWSConnector.__create_boto_3_session(profile_name=profile_name)
         if aws_session is None:
-            logger.error('Failed to connect to AWS client: ' + str(service_name))
+            logger.error("Failed to connect to AWS client: " + str(service_name))
 
         return aws_session.client(service_name=service_name)
 
@@ -91,7 +89,7 @@ class AWSConnector:
 
         aws_session = AWSConnector.__create_boto_3_session(profile_name=profile_name)
         if aws_session is None:
-            logger.error('Failed to connect to AWS client: ' + str(service_name))
+            logger.error("Failed to connect to AWS client: " + str(service_name))
 
         return aws_session.resource(service_name=service_name)
 

@@ -1,4 +1,4 @@
 # PACKAGE IMPORTS FOR EXTERNAL USAGE
 from super_gradients.common.data_connection.s3_connector import S3Connector
 
-__all__ = ['S3Connector']
+__all__ = ["S3Connector"]

@@ -2,4 +2,4 @@
 from super_gradients.common.data_interface.dataset_data_interface import DatasetDataInterface
 from super_gradients.common.data_interface.adnn_model_repository_data_interface import ADNNModelRepositoryDataInterfaces
 
-__all__ = ['DatasetDataInterface', 'ADNNModelRepositoryDataInterfaces']
+__all__ = ["DatasetDataInterface", "ADNNModelRepositoryDataInterfaces"]

@@ -1,4 +1,3 @@
-from super_gradients.common.data_types.enum import StrictLoad, DeepLearningTask, MultiGPUMode, EvaluationType,\
-    UpsampleMode
+from super_gradients.common.data_types.enum import StrictLoad, DeepLearningTask, MultiGPUMode, EvaluationType, UpsampleMode
 
-__all__ = ['StrictLoad', 'DeepLearningTask', 'EvaluationType', 'MultiGPUMode', 'UpsampleMode']
+__all__ = ["StrictLoad", "DeepLearningTask", "EvaluationType", "MultiGPUMode", "UpsampleMode"]

@@ -5,4 +5,4 @@ from super_gradients.common.data_types.enum.multi_gpu_mode import MultiGPUMode
 from super_gradients.common.data_types.enum.upsample_mode import UpsampleMode
 
 
-__all__ = ['StrictLoad', 'DeepLearningTask', 'EvaluationType', 'MultiGPUMode', 'UpsampleMode']
+__all__ = ["StrictLoad", "DeepLearningTask", "EvaluationType", "MultiGPUMode", "UpsampleMode"]

@@ -2,10 +2,10 @@ from enum import Enum
 
 
 class DeepLearningTask(str, Enum):
-    CLASSIFICATION = 'classification'
-    SEMANTIC_SEGMENTATION = 'semantic_segmentation'
-    OBJECT_DETECTION = 'object_detection'
-    DEPTH_ESTIMATION = 'depth_estimation'
-    POSE_ESTIMATION = 'pose_estimation'
-    NLP = 'nlp'
-    OTHER = 'other'
+    CLASSIFICATION = "classification"
+    SEMANTIC_SEGMENTATION = "semantic_segmentation"
+    OBJECT_DETECTION = "object_detection"
+    DEPTH_ESTIMATION = "depth_estimation"
+    POSE_ESTIMATION = "pose_estimation"
+    NLP = "nlp"
+    OTHER = "other"

@@ -12,5 +12,6 @@ class EvaluationType(str, Enum):
             VALIDATION
 
     """
-    TEST = 'TEST'
-    VALIDATION = 'VALIDATION'
+
+    TEST = "TEST"
+    VALIDATION = "VALIDATION"

@@ -2,4 +2,4 @@
 from super_gradients.common.decorators.explicit_params_validator import explicit_params_validation
 from super_gradients.common.decorators.singleton import singleton
 
-__all__ = ['explicit_params_validation', 'singleton']
+__all__ = ["explicit_params_validation", "singleton"]

@@ -1,4 +1,4 @@
-def deci_func_logger(_func=None, *, name: str = 'abstract_decorator'):
+def deci_func_logger(_func=None, *, name: str = "abstract_decorator"):
     """
     This decorator is used to wrap our functions with logs.
     It will log every enter and exit of the functon with the equivalent parameters as extras.

@@ -4,7 +4,7 @@ from typing import Callable
 
 
 class _ExplicitParamsValidator:
-    def __init__(self, function: Callable, validation_type: str = 'None'):
+    def __init__(self, function: Callable, validation_type: str = "None"):
         """
         ExplicitParamsValidator
             :param function:
@@ -23,7 +23,7 @@ class _ExplicitParamsValidator:
             :param kwargs:
             :return:
         """
-        if not hasattr(self, 'func'):
+        if not hasattr(self, "func"):
             self.func = args[0]
             return self
 
@@ -39,12 +39,12 @@ class _ExplicitParamsValidator:
         """
         var_names = inspect.getfullargspec(self.func)[0]
 
-        explicit_args_var_names = list(var_names[:len(args)])
+        explicit_args_var_names = list(var_names[: len(args)])
 
         # FOR CLASS METHOD REMOVE THE EXPLICIT DEMAND FOR self PARAMETER
         for params_list in [explicit_args_var_names, list(kwargs.keys())]:
-            if 'self' in params_list:
-                params_list.remove('self')
+            if "self" in params_list:
+                params_list.remove("self")
 
         # FIRST OF ALL HANDLE ALL OF THE KEYWORD ARGUMENTS
         for kwarg, value in kwargs.items():
@@ -63,19 +63,20 @@ class _ExplicitParamsValidator:
         :param value:
         :return:
         """
-        if self.validation_type == 'NoneOrEmpty':
+        if self.validation_type == "NoneOrEmpty":
             if not value:
-                raise ValueError('Input param: ' + str(input_param) + ' is Empty')
+                raise ValueError("Input param: " + str(input_param) + " is Empty")
 
         if value is None:
-            raise ValueError('Input param: ' + str(input_param) + ' is None')
+            raise ValueError("Input param: " + str(input_param) + " is None")
 
 
 # WRAPS THE RETRY DECORATOR CLASS TO ENABLE CALLING WITHOUT PARAMS
-def explicit_params_validation(function: Callable = None, validation_type: str = 'None'):
+def explicit_params_validation(function: Callable = None, validation_type: str = "None"):
     if function is not None:
         return _ExplicitParamsValidator(function=function)
     else:
+
         def wrapper(function):
             return _ExplicitParamsValidator(function=function, validation_type=validation_type)
 

@@ -34,5 +34,7 @@ def resolve_param(param_name: str, factory: AbstractFactory):
                         new_value = factory.get(args[index])
                         args = _assign_tuple(args, index, new_value)
             return func(*args, **kwargs)
+
         return wrapper
+
     return inner

@@ -63,7 +63,7 @@ class AbstractSGLogger(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def add_image(self, tag: str, image: Union[torch.Tensor, np.array, Image.Image], data_format: str = 'CHW', global_step: int = None):
+    def add_image(self, tag: str, image: Union[torch.Tensor, np.array, Image.Image], data_format: str = "CHW", global_step: int = None):
         """
         Add a single image to SGLogger.
         Typically, this function will add an image to tensorboard, save it to disk or add it to experiment management framework.
@@ -76,7 +76,7 @@ class AbstractSGLogger(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def add_images(self, tag: str, images: Union[torch.Tensor, np.array], data_format='NCHW', global_step: int = None):
+    def add_images(self, tag: str, images: Union[torch.Tensor, np.array], data_format="NCHW", global_step: int = None):
         """
         Add multiple images to SGLogger.
         Typically, this function will add images to tensorboard, save them to disk or add them to experiment management framework.
@@ -89,7 +89,7 @@ class AbstractSGLogger(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def add_histogram(self, tag: str, values: Union[torch.Tensor, np.array], bins: Union[str, np.array, list, int] = 'auto', global_step: int = None):
+    def add_histogram(self, tag: str, values: Union[torch.Tensor, np.array], bins: Union[str, np.array, list, int] = "auto", global_step: int = None):
         """
         Add a histogram to SGLogger.
         Typically, this function will add a histogram to tensorboard or add it to experiment management framework.