diff --git a/examples/experimental/openvino/bert/main.py b/examples/experimental/openvino/bert/main.py
index ea7a3772430..34dd31094b5 100644
--- a/examples/experimental/openvino/bert/main.py
+++ b/examples/experimental/openvino/bert/main.py
@@ -13,13 +13,13 @@
 from pathlib import Path
 from typing import Any, Iterable
 
-import datasets
 import evaluate
 import numpy as np
 import openvino.runtime as ov
 import torch
 import transformers
 
+import datasets
 import nncf
 from nncf.parameters import ModelType
 
diff --git a/nncf/config/config.py b/nncf/config/config.py
index e2793419b5a..a414ce05c96 100644
--- a/nncf/config/config.py
+++ b/nncf/config/config.py
@@ -145,5 +145,3 @@ def validate(loaded_json):
                 # Passed a list of dicts
                 for compression_algo_dict in compression_section:
                     validate_single_compression_algo_schema(compression_algo_dict, REF_VS_ALGO_SCHEMA)
-
-
diff --git a/nncf/config/extractors.py b/nncf/config/extractors.py
index 1cbf5642a8c..737a8d6caaf 100644
--- a/nncf/config/extractors.py
+++ b/nncf/config/extractors.py
@@ -226,4 +226,4 @@ def validate_accuracy_aware_schema(config: NNCFConfig, params: Dict[str, object]
 
 
 def has_input_info_field(config: NNCFConfig) -> bool:
-    return config.get('input_info') is not None
\ No newline at end of file
+    return config.get("input_info") is not None
diff --git a/nncf/config/schema.py b/nncf/config/schema.py
index a18bea73525..7ad4e085b51 100644
--- a/nncf/config/schema.py
+++ b/nncf/config/schema.py
@@ -70,8 +70,9 @@
             " during tracing and exporting.",
         ),
         "keyword": with_attributes(
-            STRING, description="Keyword to be used when passing the tensor to the model's 'forward' method - "
-                                "leave unspecified to pass the corresponding argument as a positional arg."
+            STRING,
+            description="Keyword to be used when passing the tensor to the model's 'forward' method - "
+            "leave unspecified to pass the corresponding argument as a positional arg.",
         ),
     },
     "additionalProperties": False,
diff --git a/nncf/experimental/torch/quantization/quantize_model.py b/nncf/experimental/torch/quantization/quantize_model.py
index 82e871d2d54..d7fbb3ac247 100644
--- a/nncf/experimental/torch/quantization/quantize_model.py
+++ b/nncf/experimental/torch/quantization/quantize_model.py
@@ -9,7 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, Optional, Tuple
+from typing import Optional
 
 import torch
 
@@ -20,7 +20,7 @@
 from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
 from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
 from nncf.scopes import IgnoredScope
-from nncf.torch.dynamic_graph.io_handling import get_input_infos_from_dataset
+from nncf.torch.dynamic_graph.io_handling import ExactInputsInfo
 from nncf.torch.nncf_network import NNCFNetwork
 from nncf.torch.utils import training_mode_switcher
 
@@ -35,12 +35,10 @@ def create_nncf_network_ptq(model: torch.nn.Module, dataset: Dataset) -> NNCFNet
     :return: NNCFNetwork instance for the input model
     """
 
-    input_info_list = get_input_infos_from_dataset(dataset)
+    input_info = ExactInputsInfo.from_nncf_dataset(dataset)
 
     with training_mode_switcher(model, is_training=False):
-        nncf_network = NNCFNetwork(
-            model, input_info=input_info_list
-        )
+        nncf_network = NNCFNetwork(model, input_info=input_info)
 
         nncf_network.nncf.get_tracing_context().disable_trace_dynamic_graph()
 
diff --git a/nncf/torch/dynamic_graph/graph_tracer.py b/nncf/torch/dynamic_graph/graph_tracer.py
index cc70371efa5..5eac2da368f 100644
--- a/nncf/torch/dynamic_graph/graph_tracer.py
+++ b/nncf/torch/dynamic_graph/graph_tracer.py
@@ -8,9 +8,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from collections import OrderedDict
 from copy import deepcopy
-from typing import Any, Callable, List, Optional
+from typing import Any, Callable, Optional
 
 import torch
 
@@ -19,6 +18,7 @@
 from nncf.torch.dynamic_graph.io_handling import ModelInputInfo
 from nncf.torch.utils import get_model_device
 
+
 class GraphTracer:
     def __init__(self, custom_forward_fn: Callable[[torch.nn.Module], Any]):
         self.custom_forward_fn = custom_forward_fn
diff --git a/nncf/torch/dynamic_graph/io_handling.py b/nncf/torch/dynamic_graph/io_handling.py
index 0e4d9cd13ac..bb3020329d4 100644
--- a/nncf/torch/dynamic_graph/io_handling.py
+++ b/nncf/torch/dynamic_graph/io_handling.py
@@ -9,13 +9,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import abc
-from collections import OrderedDict
 from copy import deepcopy
 from inspect import Parameter
 from inspect import Signature
-from typing import Any, List, Optional, Type, Protocol
-from typing import Tuple
-from typing import Dict, Set
+from typing import Any, Dict, List, Optional, Protocol, Set, Tuple, Type
 
 import torch
 
@@ -26,7 +23,9 @@
 from nncf.common.initialization.dataloader import NNCFDataLoader
 from nncf.common.logging import nncf_logger
 from nncf.common.utils.api_marker import api
-from nncf.config.structures import BNAdaptationInitArgs, QuantizationRangeInitArgs, NNCFExtraConfigStruct
+from nncf.config.structures import BNAdaptationInitArgs
+from nncf.config.structures import NNCFExtraConfigStruct
+from nncf.config.structures import QuantizationRangeInitArgs
 from nncf.torch.dynamic_graph.context import forward_nncf_trace
 from nncf.torch.dynamic_graph.patch_pytorch import register_operator
 from nncf.torch.initialization import wrap_dataloader_for_init
@@ -155,20 +154,27 @@ def from_nncf_config(cls, config: NNCFConfig):
         if input_infos is None:
             raise RuntimeError("Passed NNCFConfig does not have an 'input_info' field")
         if isinstance(input_infos, dict):
-            return FillerInputInfo([FillerInputElement(
-                    input_infos.get("sample_size"),
-                    input_infos.get("type"),
-                    input_infos.get("keyword"),
-                    input_infos.get("filler"))])
+            return FillerInputInfo(
+                [
+                    FillerInputElement(
+                        input_infos.get("sample_size"),
+                        input_infos.get("type"),
+                        input_infos.get("keyword"),
+                        input_infos.get("filler"),
+                    )
+                ]
+            )
         if isinstance(input_infos, list):
             elements: List[FillerInputElement] = []
             for info_dict in input_infos:
-                elements.append(FillerInputElement(
-                    info_dict.get("sample_size"),
-                    info_dict.get("type"),
-                    info_dict.get("keyword"),
-                    info_dict.get("filler")
-                ))
+                elements.append(
+                    FillerInputElement(
+                        info_dict.get("sample_size"),
+                        info_dict.get("type"),
+                        info_dict.get("keyword"),
+                        info_dict.get("filler"),
+                    )
+                )
             return FillerInputInfo(elements)
         raise RuntimeError("Invalid input_infos specified in config - should be either dict or list of dicts")
 
@@ -217,7 +223,9 @@ def from_nncf_config_dataloaders(cls, config: NNCFConfig) -> Optional["ExactInpu
 
 
 class InputInfoWrapManager:
-    def __init__(self, input_info: ModelInputInfo, fwd_signature: Signature, module_ref_for_device: torch.nn.Module = None):
+    def __init__(
+        self, input_info: ModelInputInfo, fwd_signature: Signature, module_ref_for_device: torch.nn.Module = None
+    ):
         self._fwd_signature = fwd_signature
         self._module_ref_for_device = module_ref_for_device
         args, kwargs = input_info.get_forward_inputs()
@@ -264,14 +272,9 @@ def wrap_inputs(self, model_args: Tuple, model_kwargs: Dict) -> Tuple[Tuple, Dic
         return bound_model_params.args, bound_model_params.kwargs
 
 
-
 class HasDataloader(Protocol):
     def get_dataloader(self) -> NNCFDataLoader:
         pass
 
 
-EXTRA_STRUCTS_WITH_DATALOADERS: List[Type[NNCFExtraConfigStruct]] = [
-    QuantizationRangeInitArgs,
-    BNAdaptationInitArgs
-]
-
+EXTRA_STRUCTS_WITH_DATALOADERS: List[Type[NNCFExtraConfigStruct]] = [QuantizationRangeInitArgs, BNAdaptationInitArgs]
diff --git a/nncf/torch/exporter.py b/nncf/torch/exporter.py
index d6c6bb4fa4c..9218dfcd271 100644
--- a/nncf/torch/exporter.py
+++ b/nncf/torch/exporter.py
@@ -8,7 +8,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from copy import copy
 from functools import partial
 from typing import Any, Tuple
 
@@ -139,7 +138,6 @@ def to_single_batch_tensors(obj: torch.Tensor):
         args = objwalk(args, is_tensor, to_single_batch_tensors)
         kwargs = objwalk(kwargs, is_tensor, to_single_batch_tensors)
         export_args = (*args, kwargs)  # according to a variant of passing kwargs in torch.onnx.export doc
-        num_tensors = 0
 
         if self._input_names is not None:
             input_names = self._input_names
diff --git a/nncf/torch/graph/graph_builder.py b/nncf/torch/graph/graph_builder.py
index e82f829e989..997c56b4910 100644
--- a/nncf/torch/graph/graph_builder.py
+++ b/nncf/torch/graph/graph_builder.py
@@ -9,7 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections import defaultdict
-from typing import Any, Callable, Dict, List, Optional, Set
+from typing import Any, Callable, Dict, Optional, Set
 
 import torch
 
@@ -17,7 +17,6 @@
 from nncf.torch.dynamic_graph.context import TracingContext
 from nncf.torch.dynamic_graph.graph import DynamicGraph
 from nncf.torch.dynamic_graph.graph_tracer import GraphTracer
-from nncf.torch.dynamic_graph.io_handling import ModelInputElement
 from nncf.torch.dynamic_graph.layer_attributes_handlers import set_nodes_attributes_in_nncf_graph
 from nncf.torch.dynamic_graph.scope import Scope
 from nncf.torch.graph.graph import PTNNCFGraph
@@ -29,10 +28,7 @@ def __init__(self, custom_forward_fn: Callable[[torch.nn.Module], Any]):
         self.custom_forward_fn = custom_forward_fn
 
     def build_graph(
-        self,
-        model: torch.nn.Module,
-        context_to_use: Optional[TracingContext] = None,
-        as_eval: bool = False
+        self, model: torch.nn.Module, context_to_use: Optional[TracingContext] = None, as_eval: bool = False
     ) -> PTNNCFGraph:
         tracer = GraphTracer(self.custom_forward_fn)
         dynamic_graph = tracer.trace_graph(model, context_to_use, as_eval)
diff --git a/nncf/torch/model_creation.py b/nncf/torch/model_creation.py
index 0d5b878c031..3900ba9d2c5 100644
--- a/nncf/torch/model_creation.py
+++ b/nncf/torch/model_creation.py
@@ -165,21 +165,24 @@ def get_input_info_from_config(config: NNCFConfig) -> ModelInputInfo:
     if has_input_info_field(config):
         return FillerInputInfo.from_nncf_config(config)
 
-    nncf_logger.debug("Config has no 'input_info' section, trying to use dataloader output as model inputs "
-                      "for graph building.")
+    nncf_logger.debug(
+        "Config has no 'input_info' section, trying to use dataloader output as model inputs " "for graph building."
+    )
     exact_info = ExactInputsInfo.from_nncf_config_dataloaders(config)
     if exact_info is not None:
         return exact_info
-    raise RuntimeError("Could not determine tensor inputs for the model's forward call.\n"
-                       "If you are using the `nncf.quantize` API, make sure that you supply the "
-                       "calibration dataloader to the `nncf.quantize` call.\n"
-                       "If you are using the `create_compressed_model` API, either specify the "
-                       "inputs by using the 'input_info' section in the NNCFConfig, or attach an "
-                       "initialization dataloader to the NNCFConfig by calling "
-                       "`NNCFConfig.register_extra_structs(...)` with one of the following extra "
-                       f"structures:\n"
-                       f"{EXTRA_STRUCTS_WITH_DATALOADERS}\n"
-                       f"or by calling `nncf.torch.register_default_init_args`")
+    raise RuntimeError(
+        "Could not determine tensor inputs for the model's forward call.\n"
+        "If you are using the `nncf.quantize` API, make sure that you supply the "
+        "calibration dataloader to the `nncf.quantize` call.\n"
+        "If you are using the `create_compressed_model` API, either specify the "
+        "inputs by using the 'input_info' section in the NNCFConfig, or attach an "
+        "initialization dataloader to the NNCFConfig by calling "
+        "`NNCFConfig.register_extra_structs(...)` with one of the following extra "
+        f"structures:\n"
+        f"{EXTRA_STRUCTS_WITH_DATALOADERS}\n"
+        f"or by calling `nncf.torch.register_default_init_args`"
+    )
 
 
 def create_nncf_network_with_inputs_from_config(model: torch.nn.Module, config: NNCFConfig):
@@ -315,4 +318,3 @@ def create_compression_algorithm_builder_from_algo_names(
     else:
         builder = PTCompositeCompressionAlgorithmBuilder(config, should_init=should_init)
     return builder
-
diff --git a/nncf/torch/nncf_network.py b/nncf/torch/nncf_network.py
index b2542392ccf..5b3279a084e 100644
--- a/nncf/torch/nncf_network.py
+++ b/nncf/torch/nncf_network.py
@@ -41,7 +41,7 @@
 from nncf.torch.dynamic_graph.graph import ShapeIgnoringTensorMetaComparator
 from nncf.torch.dynamic_graph.graph_tracer import GraphTracer
 from nncf.torch.dynamic_graph.graph_tracer import create_dummy_forward_fn
-from nncf.torch.dynamic_graph.io_handling import InputInfoWrapManager, ModelInputElement
+from nncf.torch.dynamic_graph.io_handling import InputInfoWrapManager
 from nncf.torch.dynamic_graph.io_handling import ModelInputInfo
 from nncf.torch.dynamic_graph.io_handling import replicate_same_tensors
 from nncf.torch.dynamic_graph.io_handling import wrap_nncf_model_outputs_with_objwalk
@@ -499,9 +499,7 @@ def rebuild_graph(self, *input_args):
         builder = GraphBuilder(dummy_forward_fn)
 
         with training_mode_switcher(self._model_ref, is_training=False):
-            self._compressed_graph = builder.build_graph(
-                self._model_ref, self._compressed_context
-            )
+            self._compressed_graph = builder.build_graph(self._model_ref, self._compressed_context)
 
     def is_scope_in_nncf_module_scope(self, scope: Scope) -> bool:
         norm_nncf_scopes = []
diff --git a/nncf/torch/quantization/quantize_model.py b/nncf/torch/quantization/quantize_model.py
index f23c9786983..fad8fefeb9f 100644
--- a/nncf/torch/quantization/quantize_model.py
+++ b/nncf/torch/quantization/quantize_model.py
@@ -39,6 +39,7 @@ class CalibrationDataLoaderAdapter(PTInitializingDataLoader):
     """
     An adapter from the nncf.Dataset to the PTInitializingDataLoader.
     """
+
     def get_target(self, dataloader_output: Any) -> Any:
         raise NotImplementedError
 
@@ -216,10 +217,7 @@ def quantize_impl(
     )
 
     clone_model = deepcopy(model)
-    compression_ctrl, compressed_model = create_compressed_model(
-        model=clone_model,
-        config=nncf_config
-    )
+    compression_ctrl, compressed_model = create_compressed_model(model=clone_model, config=nncf_config)
     compression_ctrl.prepare_for_export()
     compressed_model.nncf.disable_dynamic_graph_building()
 
diff --git a/tests/torch/automl/test_quantization_env.py b/tests/torch/automl/test_quantization_env.py
index eec2c6eaa16..15bdc40f79f 100644
--- a/tests/torch/automl/test_quantization_env.py
+++ b/tests/torch/automl/test_quantization_env.py
@@ -22,7 +22,7 @@
 from nncf.torch.automl.environment.quantization_env import ModelSizeCalculator
 from nncf.torch.automl.environment.quantization_env import QuantizationEnv
 from nncf.torch.automl.environment.quantization_env import QuantizationEnvParams
-
+from nncf.torch.dynamic_graph.io_handling import FillerInputInfo
 from nncf.torch.hardware.config import PTHWConfig
 from nncf.torch.nncf_network import NNCFNetwork
 from nncf.torch.quantization.algo import ExperimentalQuantizationBuilder
@@ -38,7 +38,7 @@ def create_test_quantization_env(model_creator=BasicConvTestModel, input_info_cf
         input_info_cfg = {"input_info": {"sample_size": [1, 1, 4, 4]}}
 
     model = model_creator()
-    nncf_network = NNCFNetwork(model, input_info=FillerInputInfo(input_info_cfg))
+    nncf_network = NNCFNetwork(model, input_info=FillerInputInfo.from_nncf_config(input_info_cfg))
     hw_config_type = HWConfigType.VPU
     hw_config_path = HWConfig.get_path_to_hw_config(hw_config_type)
     hw_config = PTHWConfig.from_json(hw_config_path)
diff --git a/tests/torch/helpers.py b/tests/torch/helpers.py
index 3b95a56c4e9..f984cb2d58f 100644
--- a/tests/torch/helpers.py
+++ b/tests/torch/helpers.py
@@ -31,7 +31,7 @@
 from nncf.config.structures import BNAdaptationInitArgs
 from nncf.torch.algo_selector import PT_COMPRESSION_ALGORITHMS
 from nncf.torch.compression_method_api import PTCompressionAlgorithmController
-
+from nncf.torch.dynamic_graph.io_handling import FillerInputInfo
 from nncf.torch.dynamic_graph.scope import Scope
 from nncf.torch.initialization import PTInitializingDataLoader
 from nncf.torch.initialization import register_default_init_args
@@ -374,8 +374,8 @@ def __getitem__(self, index):
 def create_any_mock_dataloader(
     dataset_cls: type, config: NNCFConfig, num_samples: int = 1, batch_size: int = 1
 ) -> DataLoader:
-    input_infos_list = FillerInputInfo(config)
-    input_sample_size = input_infos_list[0].shape
+    input_info = FillerInputInfo.from_nncf_config(config)
+    input_sample_size = input_info.elements[0].shape
     data_loader = DataLoader(
         dataset_cls(input_sample_size[1:], num_samples),
         batch_size=batch_size,
diff --git a/tests/torch/nas/creators.py b/tests/torch/nas/creators.py
index f2a768aeefa..8934b4b636d 100644
--- a/tests/torch/nas/creators.py
+++ b/tests/torch/nas/creators.py
@@ -27,7 +27,6 @@
 )
 from nncf.torch.dynamic_graph.graph_tracer import create_dummy_forward_fn
 from nncf.torch.dynamic_graph.io_handling import FillerInputInfo
-
 from nncf.torch.graph.transformations.layout import PTTransformationLayout
 from nncf.torch.model_creation import create_nncf_network_with_inputs_from_config
 from nncf.torch.model_transformer import PTModelTransformer
diff --git a/tests/torch/ptq/test_quantize_model_helpers.py b/tests/torch/ptq/test_quantize_model_helpers.py
index 30b7d2e4742..5c688933d5c 100644
--- a/tests/torch/ptq/test_quantize_model_helpers.py
+++ b/tests/torch/ptq/test_quantize_model_helpers.py
@@ -13,7 +13,7 @@
 from torch import nn
 
 from nncf.data import Dataset
-from nncf.experimental.torch.quantization.quantize_model import create_nncf_network_with_config_ptq
+from nncf.experimental.torch.quantization.quantize_model import create_nncf_network_ptq
 
 
 class TestModel(nn.Module):
@@ -35,7 +35,7 @@ def transform_fn(inputs):
         return x
 
     dataset = Dataset([(torch.empty(input_shape), 1)] * 3, transform_fn)
-    nncf_network = create_nncf_network_with_config_ptq(model, dataset)
+    nncf_network = create_nncf_network_ptq(model, dataset)
     nncf_graph = nncf_network.nncf.get_original_graph()
     all_nodes = nncf_graph.get_all_nodes()
     assert len(all_nodes) == 2
diff --git a/tests/torch/quantization/quantization_helpers.py b/tests/torch/quantization/quantization_helpers.py
index d9d1d7cb021..a8f5f213552 100644
--- a/tests/torch/quantization/quantization_helpers.py
+++ b/tests/torch/quantization/quantization_helpers.py
@@ -11,7 +11,7 @@
 import torch
 
 from nncf import NNCFConfig
-
+from nncf.torch.dynamic_graph.io_handling import FillerInputInfo
 from tests.torch.helpers import get_empty_config
 
 
@@ -75,8 +75,8 @@ def distributed_init_test_default(gpu, ngpus_per_node, config):
 
 
 def create_rank_dataloader(config, rank, num_samples=10, batch_size=3):
-    input_infos_list = FillerInputInfo(config)
-    input_sample_size = input_infos_list[0].shape
+    input_infos_list = FillerInputInfo.from_nncf_config(config)
+    input_sample_size = input_infos_list.elements[0].shape
     data_loader = torch.utils.data.DataLoader(
         RankDatasetMock(input_sample_size[1:], rank, num_samples),
         batch_size=batch_size,
diff --git a/tests/torch/quantization/test_hawq_precision_init.py b/tests/torch/quantization/test_hawq_precision_init.py
index 9284296c311..0d8542537eb 100644
--- a/tests/torch/quantization/test_hawq_precision_init.py
+++ b/tests/torch/quantization/test_hawq_precision_init.py
@@ -29,6 +29,7 @@
 from examples.common.sample_config import SampleConfig
 from examples.torch.classification.main import create_cifar
 from examples.torch.object_detection.models.ssd_vgg import SSD_VGG
+from nncf import NNCFConfig
 from nncf.common.graph import NNCFNodeName
 from nncf.common.hardware.config import HWConfigType
 from nncf.common.quantization.quantizer_setup import SingleConfigQuantizerSetup
@@ -36,7 +37,7 @@
 from nncf.common.utils.debug import set_debug_log_dir
 from nncf.torch import register_default_init_args
 from nncf.torch.checkpoint_loading import load_state
-
+from nncf.torch.dynamic_graph.io_handling import FillerInputInfo
 from nncf.torch.initialization import default_criterion_fn
 from nncf.torch.quantization.adjust_padding import add_adjust_padding_nodes
 from nncf.torch.quantization.hessian_trace import HessianTraceEstimator
@@ -78,8 +79,8 @@
 from tests.torch.test_models.mobilenet import mobilenet_v2
 
 
-def create_test_dataloaders(config, dataset_dir):
-    input_info = FillerInputInfo(config)[0]
+def create_test_dataloaders(config: NNCFConfig, dataset_dir):
+    input_info = FillerInputInfo.from_nncf_config(config).elements[0]
     image_size = input_info.shape[-1]
     batch_size = input_info.shape[0]
     normalize = transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
diff --git a/tests/torch/quantization/test_scheduler.py b/tests/torch/quantization/test_scheduler.py
index 9c474d82d58..9f0ed0b6924 100644
--- a/tests/torch/quantization/test_scheduler.py
+++ b/tests/torch/quantization/test_scheduler.py
@@ -15,7 +15,7 @@
 from nncf.common.statistics import NNCFStatistics
 from nncf.config.structures import QuantizationRangeInitArgs
 from nncf.torch import register_default_init_args
-
+from nncf.torch.dynamic_graph.io_handling import FillerInputInfo
 from nncf.torch.initialization import wrap_dataloader_for_init
 from nncf.torch.quantization.base_ctrl import QuantizationControllerBase
 from nncf.torch.quantization.schedulers import StagedQuantizationScheduler
@@ -192,8 +192,8 @@ def test_staged_scheduler_with_range_init():
     register_bn_adaptation_init_args(config)
     model = squeezenet1_1(num_classes=10, dropout=0)
 
-    input_infos_list = FillerInputInfo(config)
-    input_sample_size = input_infos_list[0].shape
+    input_infos_list = FillerInputInfo.from_nncf_config(config)
+    input_sample_size = input_infos_list.elements[0].shape
     data_loader = DataLoader(
         OnesDatasetMock(input_sample_size[1:]),
         batch_size=1,
@@ -254,8 +254,8 @@ def test_staged_scheduler_with_hawq():
     num_classes = 10
     model = squeezenet1_1(num_classes=num_classes, dropout=0)
 
-    input_infos_list = FillerInputInfo(config)
-    input_sample_size = input_infos_list[0].shape
+    input_infos_list = FillerInputInfo.from_nncf_config(config)
+    input_sample_size = input_infos_list.elements[0].shape
     data_loader = DataLoader(
         HawqDatasetMock(input_sample_size[1:], num_classes),
         batch_size=1,
diff --git a/tests/torch/sparsity/movement/helpers/run_recipe.py b/tests/torch/sparsity/movement/helpers/run_recipe.py
index 89ca9d4658f..f5f172a55fb 100644
--- a/tests/torch/sparsity/movement/helpers/run_recipe.py
+++ b/tests/torch/sparsity/movement/helpers/run_recipe.py
@@ -20,7 +20,6 @@
 import torch.nn
 import torch.nn.functional as F
 import torch.utils.data
-from datasets import Dataset  # pylint: disable=no-name-in-module
 from transformers import AutoModelForAudioClassification
 from transformers import AutoModelForImageClassification
 from transformers import AutoModelForSequenceClassification
@@ -34,6 +33,7 @@
 from transformers import SwinConfig
 from transformers import Wav2Vec2Config
 
+from datasets import Dataset  # pylint: disable=no-name-in-module
 from nncf import NNCFConfig
 from nncf.experimental.torch.sparsity.movement.scheduler import MovementSchedulerParams
 from nncf.torch.dynamic_graph.io_handling import FillerInputElement
@@ -482,7 +482,9 @@ class SwinRunRecipe(BaseMockRunRecipe):
     @property
     def model_input_info(self) -> ModelInputInfo:
         img_size = self.model_config.image_size
-        return FillerInputInfo([FillerInputElement(shape=[1, self.model_config.num_channels, img_size, img_size], keyword="pixel_values")])
+        return FillerInputInfo(
+            [FillerInputElement(shape=[1, self.model_config.num_channels, img_size, img_size], keyword="pixel_values")]
+        )
 
     @property
     def transformer_block_info(self) -> List[TransformerBlockInfo]:
diff --git a/tests/torch/sparsity/movement/helpers/trainer.py b/tests/torch/sparsity/movement/helpers/trainer.py
index 7da241bf712..63e62f5e0db 100644
--- a/tests/torch/sparsity/movement/helpers/trainer.py
+++ b/tests/torch/sparsity/movement/helpers/trainer.py
@@ -14,7 +14,6 @@
 
 import numpy as np
 import torch
-from datasets import Dataset  # pylint: disable=no-name-in-module
 from transformers import TrainingArguments
 from transformers.trainer import Trainer
 from transformers.trainer_callback import TrainerCallback
@@ -22,6 +21,7 @@
 from transformers.trainer_callback import TrainerState
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
 
+from datasets import Dataset  # pylint: disable=no-name-in-module
 from nncf.api.compression import CompressionAlgorithmController
 from nncf.common.compression import BaseCompressionAlgorithmController
 from nncf.common.utils.tensorboard import prepare_for_tensorboard
diff --git a/tests/torch/sparsity/movement/test_model_saving.py b/tests/torch/sparsity/movement/test_model_saving.py
index c20a5cc6255..4fc6ee4d044 100644
--- a/tests/torch/sparsity/movement/test_model_saving.py
+++ b/tests/torch/sparsity/movement/test_model_saving.py
@@ -18,7 +18,6 @@
 import pytest
 import torch
 from addict import Dict
-from datasets import Dataset  # pylint: disable=no-name-in-module
 from onnx import numpy_helper
 from openvino.runtime import Core
 from openvino.runtime import serialize
@@ -29,6 +28,7 @@
 from scipy.special import softmax
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
 
+from datasets import Dataset  # pylint: disable=no-name-in-module
 from nncf.torch import create_compressed_model
 from nncf.torch.checkpoint_loading import load_state
 from tests.torch.helpers import PTTensorListComparator
diff --git a/tests/torch/sparsity/movement/training_scripts/run_glue.py b/tests/torch/sparsity/movement/training_scripts/run_glue.py
index 09a6bf6c737..de6735e011d 100644
--- a/tests/torch/sparsity/movement/training_scripts/run_glue.py
+++ b/tests/torch/sparsity/movement/training_scripts/run_glue.py
@@ -12,12 +12,13 @@
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
-import datasets
 import evaluate
 import jstyleson
 import numpy as np
 from transformers.training_args import ParallelMode
 
+import datasets
+
 # isort: off
 from nncf import NNCFConfig
 from nncf.api.compression import CompressionAlgorithmController
diff --git a/tests/torch/test_compressed_graph.py b/tests/torch/test_compressed_graph.py
index 6314389503f..31b4ce8c13d 100644
--- a/tests/torch/test_compressed_graph.py
+++ b/tests/torch/test_compressed_graph.py
@@ -30,8 +30,8 @@
 from nncf.common.quantization.quantizer_setup import SingleConfigQuantizerSetup
 from nncf.torch import nncf_model_input
 from nncf.torch import nncf_model_output
-from nncf.torch.dynamic_graph.io_handling import FillerInputElement
 from nncf.torch.dynamic_graph.graph_tracer import create_dummy_forward_fn
+from nncf.torch.dynamic_graph.io_handling import FillerInputElement
 from nncf.torch.dynamic_graph.io_handling import FillerInputInfo
 from nncf.torch.graph.graph import PTNNCFGraph
 from nncf.torch.graph.graph_builder import GraphBuilder
diff --git a/tests/torch/test_graph_building.py b/tests/torch/test_graph_building.py
index ab6f9da1125..74bf01cf13e 100644
--- a/tests/torch/test_graph_building.py
+++ b/tests/torch/test_graph_building.py
@@ -10,10 +10,7 @@
 # limitations under the License.
 from copy import deepcopy
 from dataclasses import dataclass
-from typing import Dict
-from typing import List, Tuple
-from typing import Optional
-from typing import Union
+from typing import Dict, List, Tuple, Union
 from unittest.mock import MagicMock
 
 import pytest
@@ -89,10 +86,7 @@ def forward(self, x):
 
     mod = Model()
 
-    tracer = GraphTracer(
-        custom_forward_fn=create_dummy_forward_fn(FillerInputInfo([FillerInputElement([1, 1, 1, 1])])
-        )
-    )
+    tracer = GraphTracer(custom_forward_fn=create_dummy_forward_fn(FillerInputInfo([FillerInputElement([1, 1, 1, 1])])))
     graph = tracer.trace_graph(mod)
 
     unique_op_exec_contexts = set()
@@ -187,9 +181,11 @@ def test_activation_shape_tracing(input_shape: Tuple[int, ...]):
     model = ModelForTest()
     graph_builder = GraphBuilder(
         create_dummy_forward_fn(
-            FillerInputInfo([
-                FillerInputElement(input_shape),
-            ]),
+            FillerInputInfo(
+                [
+                    FillerInputElement(input_shape),
+                ]
+            ),
             with_input_tracing=True,
             with_output_tracing=True,
         )
@@ -258,7 +254,8 @@ def forward(self, x):
 def test_concat_attributes_saved_during_graph_building(input_shape):
     model = ModelForTestWithReshapeFlattenAndConcat()
     graph_builder = GraphBuilder(
-        create_dummy_forward_fn(FillerInputInfo([FillerInputElement(input_shape)]),
+        create_dummy_forward_fn(
+            FillerInputInfo([FillerInputElement(input_shape)]),
             with_input_tracing=True,
             with_output_tracing=True,
         )
@@ -288,9 +285,10 @@ def test_reshape_attributes_saved_during_graph_building(input_shape):
     graph_builder = GraphBuilder(
         create_dummy_forward_fn(
             FillerInputInfo(
-            [
-                FillerInputElement(input_shape),
-            ]),
+                [
+                    FillerInputElement(input_shape),
+                ]
+            ),
             with_input_tracing=True,
             with_output_tracing=True,
         )
@@ -341,9 +339,11 @@ def test_permute_attributes_saved_during_graph_building(input_shape):
     model = ModelWithPermute()
     graph_builder = GraphBuilder(
         create_dummy_forward_fn(
-            FillerInputInfo([
-                FillerInputElement(input_shape),
-            ]),
+            FillerInputInfo(
+                [
+                    FillerInputElement(input_shape),
+                ]
+            ),
             with_input_tracing=True,
             with_output_tracing=True,
         )
@@ -394,9 +394,11 @@ def test_split_attributes(input_shape):
     model = ModelForTestWithSplit(input_shape)
     graph_builder = GraphBuilder(
         create_dummy_forward_fn(
-            FillerInputInfo([
-                FillerInputElement(input_shape),
-            ]),
+            FillerInputInfo(
+                [
+                    FillerInputElement(input_shape),
+                ]
+            ),
             with_input_tracing=True,
             with_output_tracing=True,
         )
@@ -429,9 +431,11 @@ def forward(self, x):
 def test_getitem_attributes(input_shape):
     model = SplitByGetItemModel()
     custom_forward_fn = create_dummy_forward_fn(
-        FillerInputInfo([
-            FillerInputElement(input_shape),
-        ]),
+        FillerInputInfo(
+            [
+                FillerInputElement(input_shape),
+            ]
+        ),
         with_input_tracing=True,
         with_output_tracing=True,
     )
@@ -476,9 +480,11 @@ def _get_default_nncf_graph_edge(from_node, to_node, input_port_id, output_port_
     model = ParallelEdgesModel()
     graph_builder = GraphBuilder(
         create_dummy_forward_fn(
-            FillerInputInfo([
-                FillerInputElement(input_shape),
-            ]),
+            FillerInputInfo(
+                [
+                    FillerInputElement(input_shape),
+                ]
+            ),
             with_input_tracing=True,
             with_output_tracing=True,
         )
@@ -502,7 +508,6 @@ def _get_default_nncf_graph_edge(from_node, to_node, input_port_id, output_port_
     assert set(input_node_output_edges) == ref_output_edges
 
 
-
 class MockModel(torch.nn.Module):
     def __init__(self, stub_forward):
         super().__init__()
@@ -512,6 +517,7 @@ def __init__(self, stub_forward):
     def forward(self, *args, **kwargs):
         return self.stub_forward(*args, **kwargs)
 
+
 class RandomRefTensor:
     def __init__(self, shape: List[int]):
         self.tensor = torch.rand(shape)
@@ -525,8 +531,9 @@ def check_arg(test_arg: torch.Tensor, ref_arg: Union[torch.Tensor, RandomRefTens
     else:
         assert torch.allclose(test_arg, ref_arg)
 
+
 class MockInputInfo(ModelInputInfo):
-    MOCK_ARGS = (torch.Tensor([42.0]), )
+    MOCK_ARGS = (torch.Tensor([42.0]),)
     MOCK_KWARGS = {"foo": torch.ones([1, 3])}
 
     def get_forward_inputs(self, device: str = None) -> Tuple[Tuple, Dict]:
@@ -553,6 +560,7 @@ def test_input_info_args_are_passed_into_forward(mocker):
     for keyword, arg in forward_call_kwargs.items():
         check_arg(arg, ref_kwargs[keyword])
 
+
 @dataclass
 class FillerInputInfoGenerationTestStruct:
     config_input_info_subdict: Union[List[Dict], Dict]
@@ -566,8 +574,8 @@ class FillerInputInfoGenerationTestStruct:
 FILLER_GEN_TEST_STRUCTS = [
     FillerInputInfoGenerationTestStruct(
         config_input_info_subdict={"sample_size": [2, 3, 300, 300], "type": "float", "filler": "zeros"},
-        ref_args=(torch.zeros([2, 3, 300, 300]), ),
-        ref_kwargs={}
+        ref_args=(torch.zeros([2, 3, 300, 300]),),
+        ref_kwargs={},
     ),
     FillerInputInfoGenerationTestStruct(
         config_input_info_subdict=[
@@ -575,17 +583,20 @@ class FillerInputInfoGenerationTestStruct:
             {"sample_size": [1, 128], "type": "long", "filler": "ones"},
             {"sample_size": [1, 128], "type": "long", "filler": "zeros"},
         ],
-        ref_args=(torch.ones([1, 128], dtype=torch.long),
-                  torch.ones([1, 128], dtype=torch.long),
-                  torch.zeros([1, 128], dtype=torch.long)),
-        ref_kwargs={}),
+        ref_args=(
+            torch.ones([1, 128], dtype=torch.long),
+            torch.ones([1, 128], dtype=torch.long),
+            torch.zeros([1, 128], dtype=torch.long),
+        ),
+        ref_kwargs={},
+    ),
     FillerInputInfoGenerationTestStruct(
         config_input_info_subdict=[
             {"sample_size": [2, 3, 300, 300], "type": "float", "filler": "zeros"},
             {"sample_size": [1, 128], "type": "long", "filler": "ones", "keyword": TEST_KEYWORD_1},
         ],
         ref_args=(torch.zeros([2, 3, 300, 300]),),
-        ref_kwargs={TEST_KEYWORD_1: torch.ones([1, 128], dtype=torch.long)}
+        ref_kwargs={TEST_KEYWORD_1: torch.ones([1, 128], dtype=torch.long)},
     ),
     FillerInputInfoGenerationTestStruct(
         config_input_info_subdict=[
@@ -593,15 +604,17 @@ class FillerInputInfoGenerationTestStruct:
             {"sample_size": [2, 3, 300, 300], "type": "float", "filler": "zeros"},
             {"sample_size": [1, 128], "type": "long", "filler": "ones", "keyword": TEST_KEYWORD_2},
         ],
-        ref_args=(torch.zeros([2, 3, 300, 300]), ),
-        ref_kwargs={TEST_KEYWORD_1: RandomRefTensor([8, 7]),
-                    TEST_KEYWORD_2: torch.ones([1, 128], dtype=torch.long)})
+        ref_args=(torch.zeros([2, 3, 300, 300]),),
+        ref_kwargs={TEST_KEYWORD_1: RandomRefTensor([8, 7]), TEST_KEYWORD_2: torch.ones([1, 128], dtype=torch.long)},
+    ),
 ]
 
 
 @pytest.mark.parametrize("filler_gen_test_struct", FILLER_GEN_TEST_STRUCTS)
 def test_filler_input_info_arg_generation(filler_gen_test_struct: FillerInputInfoGenerationTestStruct):
-    filler_input_info = FillerInputInfo.from_nncf_config(NNCFConfig.from_dict({"input_info": filler_gen_test_struct.config_input_info_subdict}))
+    filler_input_info = FillerInputInfo.from_nncf_config(
+        NNCFConfig.from_dict({"input_info": filler_gen_test_struct.config_input_info_subdict})
+    )
     test_args, test_kwargs = filler_input_info.get_forward_inputs()
 
     for test_arg, ref_arg in zip(test_args, filler_gen_test_struct.ref_args):
@@ -754,9 +767,11 @@ def test_get_all_nodes():
 
     builder = GraphBuilder(
         create_dummy_forward_fn(
-            FillerInputInfo([
-                FillerInputElement((1, 1, 4, 4)),
-            ])
+            FillerInputInfo(
+                [
+                    FillerInputElement((1, 1, 4, 4)),
+                ]
+            )
         )
     )
     graph = builder.build_graph(model)
@@ -784,9 +799,11 @@ def forward(self, x: torch.Tensor):
 
 
 def test_integer_path_marking():
-    input_infos = FillerInputInfo([
-        FillerInputElement(ModelWithIntegerPaths.INPUT_SHAPE),
-    ])
+    input_infos = FillerInputInfo(
+        [
+            FillerInputElement(ModelWithIntegerPaths.INPUT_SHAPE),
+        ]
+    )
     builder = GraphBuilder(create_dummy_forward_fn(input_infos))
     nncf_graph = builder.build_graph(ModelWithIntegerPaths())
     edges = list(nncf_graph.get_all_edges())
diff --git a/tests/torch/test_input_management.py b/tests/torch/test_input_management.py
index 9ee5e318bfe..bcbb3ade7f4 100644
--- a/tests/torch/test_input_management.py
+++ b/tests/torch/test_input_management.py
@@ -11,13 +11,15 @@
 
 import inspect
 from dataclasses import dataclass
-from typing import Tuple, Dict, List
+from typing import Dict, List, Tuple
 
 import pytest
 import torch
 
-from nncf.torch.dynamic_graph.io_handling import InputInfoWrapManager, FillerInputInfo, FillerInputElement, \
-    ModelInputInfo
+from nncf.torch.dynamic_graph.io_handling import FillerInputElement
+from nncf.torch.dynamic_graph.io_handling import FillerInputInfo
+from nncf.torch.dynamic_graph.io_handling import InputInfoWrapManager
+from nncf.torch.dynamic_graph.io_handling import ModelInputInfo
 from tests.torch.helpers import MockModel
 from tests.torch.helpers import create_compressed_model_and_algo_for_test
 from tests.torch.helpers import register_bn_adaptation_init_args
@@ -36,6 +38,7 @@
 def forward(arg1=None, arg2=None, arg3=None, arg4=None, arg5=TENSOR_DEFAULT):
     pass
 
+
 @dataclass
 class InputWrappingTestStruct:
     input_info: ModelInputInfo
@@ -54,36 +57,38 @@ def get_case_id(self) -> str:
         model_args=(TENSOR_1,),
         model_kwargs={},
         ref_wrapping_sequence=[TENSOR_1],
-        case_id="single_arg"
+        case_id="single_arg",
     ),
     InputWrappingTestStruct(
         input_info=FillerInputInfo([FillerInputElement([1], keyword="arg2")]),
         model_args=(),
         model_kwargs={"arg2": TENSOR_2},
         ref_wrapping_sequence=[TENSOR_2],
-        case_id="single_kwarg"
+        case_id="single_kwarg",
     ),
     InputWrappingTestStruct(
-        input_info=FillerInputInfo([
-            FillerInputElement([1]),
-            FillerInputElement([1]),
-            FillerInputElement([1], keyword="arg3"),
-            FillerInputElement([1], keyword="arg5"),
-        ]),
+        input_info=FillerInputInfo(
+            [
+                FillerInputElement([1]),
+                FillerInputElement([1]),
+                FillerInputElement([1], keyword="arg3"),
+                FillerInputElement([1], keyword="arg5"),
+            ]
+        ),
         model_args=(TENSOR_1, TENSOR_2),
         model_kwargs={"arg3": TENSOR_3, "arg5": TENSOR_4},
         ref_wrapping_sequence=[TENSOR_1, TENSOR_2, TENSOR_3, TENSOR_4],
-        case_id="args_and_kwargs"
+        case_id="args_and_kwargs",
     ),
     # More args supplied than what is specified by input_infos - ignore the unspecified args
     InputWrappingTestStruct(
-        input_info=FillerInputInfo([FillerInputElement([1]),
-                                    FillerInputElement([1], keyword="arg3"),
-                                    FillerInputElement([1], keyword="arg5")]),
+        input_info=FillerInputInfo(
+            [FillerInputElement([1]), FillerInputElement([1], keyword="arg3"), FillerInputElement([1], keyword="arg5")]
+        ),
         model_args=(TENSOR_1, TENSOR_2),
         model_kwargs={"arg3": TENSOR_3, "arg5": TENSOR_4},
         ref_wrapping_sequence=[TENSOR_1, TENSOR_3, TENSOR_4],
-        case_id="more_args_than_specified"
+        case_id="more_args_than_specified",
     ),
     # More args and kwargs supplied than what is specified by input_infos - ignore the unspecified args and kwargs
     InputWrappingTestStruct(
@@ -91,7 +96,7 @@ def get_case_id(self) -> str:
         model_args=(TENSOR_1, TENSOR_2),
         model_kwargs={"arg4": TENSOR_3, "arg5": TENSOR_4},
         ref_wrapping_sequence=[TENSOR_1, TENSOR_3],
-        case_id="more_args_and_kwargs_than_specified"
+        case_id="more_args_and_kwargs_than_specified",
     ),
     # arg specified, but kwarg supplied
     InputWrappingTestStruct(
@@ -99,14 +104,14 @@ def get_case_id(self) -> str:
         model_args=(),
         model_kwargs={"arg3": TENSOR_1},
         ref_wrapping_sequence=[TENSOR_FROM_INPUT_INFO_1],
-        case_id="kwarg_instead_of_arg"
+        case_id="kwarg_instead_of_arg",
     ),
     InputWrappingTestStruct(
         input_info=FillerInputInfo([FillerInputElement([1], keyword="arg5")]),
         model_args=(),
         model_kwargs={"arg1": TENSOR_1},
         ref_wrapping_sequence=[TENSOR_DEFAULT],
-        case_id="arg_as_kwarg"
+        case_id="arg_as_kwarg",
     ),
     # kwarg specified, but missing in supplied kwargs
     InputWrappingTestStruct(
@@ -114,33 +119,36 @@ def get_case_id(self) -> str:
         model_args=(TENSOR_1, TENSOR_2),
         model_kwargs={"arg4": TENSOR_3, "arg5": TENSOR_4},
         ref_wrapping_sequence=[TENSOR_1, TENSOR_FROM_INPUT_INFO_2],
-        case_id="missing_kwarg"
+        case_id="missing_kwarg",
     ),
     # More args specified than supplied
     InputWrappingTestStruct(
-        input_info=FillerInputInfo([FillerInputElement([1]),
-                                    FillerInputElement([2]),
-                                    FillerInputElement([3], keyword="arg3")]),
+        input_info=FillerInputInfo(
+            [FillerInputElement([1]), FillerInputElement([2]), FillerInputElement([3], keyword="arg3")]
+        ),
         model_args=(TENSOR_1,),
         model_kwargs={"arg3": TENSOR_2},
         ref_wrapping_sequence=[TENSOR_1, TENSOR_FROM_INPUT_INFO_2, TENSOR_2],
-        case_id="less_args_supplied"
+        case_id="less_args_supplied",
     ),
     # More kwargs specified than supplied
     InputWrappingTestStruct(
-        input_info=FillerInputInfo([FillerInputElement([1]),
-                                    FillerInputElement([2], keyword="arg2"),
-                                    FillerInputElement([3], keyword="arg3")]),
+        input_info=FillerInputInfo(
+            [FillerInputElement([1]), FillerInputElement([2], keyword="arg2"), FillerInputElement([3], keyword="arg3")]
+        ),
         model_args=(TENSOR_1,),
         model_kwargs={"arg2": TENSOR_2},
         ref_wrapping_sequence=[TENSOR_1, TENSOR_2, TENSOR_FROM_INPUT_INFO_3],
-        case_id="less_kwargs_supplied"
+        case_id="less_kwargs_supplied",
     ),
 ]
 
 
-@pytest.fixture(params=INPUT_WRAPPING_TEST_CASES, name="inputs_test_struct",
-                ids=[x.get_case_id() for x in INPUT_WRAPPING_TEST_CASES])
+@pytest.fixture(
+    params=INPUT_WRAPPING_TEST_CASES,
+    name="inputs_test_struct",
+    ids=[x.get_case_id() for x in INPUT_WRAPPING_TEST_CASES],
+)
 def inputs_test_struct_(request):
     return request.param
 
diff --git a/tests/torch/test_layer_attributes.py b/tests/torch/test_layer_attributes.py
index e23075e8eae..ea08c336a4d 100644
--- a/tests/torch/test_layer_attributes.py
+++ b/tests/torch/test_layer_attributes.py
@@ -8,7 +8,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Callable, List, Optional, Type
+from typing import Callable, Optional, Type
 
 import pytest
 from torch import Size
diff --git a/tests/torch/test_nncf_network.py b/tests/torch/test_nncf_network.py
index df83723bfac..141445f7865 100644
--- a/tests/torch/test_nncf_network.py
+++ b/tests/torch/test_nncf_network.py
@@ -74,9 +74,11 @@ def forward(self, inputs):
 
     qnet_no_shape = NNCFNetwork(
         deepcopy(model),
-        input_info=FillerInputInfo([
-            FillerInputElement(input_shape_1),
-        ]),
+        input_info=FillerInputInfo(
+            [
+                FillerInputElement(input_shape_1),
+            ]
+        ),
         scopes_without_shape_matching=["MatMulModel"],
     )
 
@@ -95,9 +97,11 @@ def forward(self, inputs):
 
     qnet = NNCFNetwork(
         model,
-        input_info=FillerInputInfo([
-            FillerInputElement(input_shape_1),
-        ]),
+        input_info=FillerInputInfo(
+            [
+                FillerInputElement(input_shape_1),
+            ]
+        ),
     )
     context = qnet.nncf.get_tracing_context()
     context.enable_trace_dynamic_graph()
@@ -240,8 +244,7 @@ def test_get_weighted_original_graph_nodes():
 # pylint: disable=protected-access
 def test_get_op_nodes_in_scope():
     model = TwoConvTestModel()
-    nncf_model = NNCFNetwork(deepcopy(model),
-                             input_info=FillerInputInfo([FillerInputElement([1, 1, 4, 4])]))
+    nncf_model = NNCFNetwork(deepcopy(model), input_info=FillerInputInfo([FillerInputElement([1, 1, 4, 4])]))
     nncf_graph = nncf_model.nncf.get_original_graph()
 
     # Valid scopes should be successfully found
@@ -622,7 +625,8 @@ def test_forward_signature_is_same_as_for_original_model(simple_net):
     # Verify that if we create 2 NNCFNetworks, then each will have its own signature
     another_original_obj = MultiInputModel()
     another_nncf_net = NNCFNetwork(
-        MultiInputModel(), input_info=FillerInputInfo([FillerInputElement([1, 1, 1, 1]), FillerInputElement([1, 1, 1, 1])])
+        MultiInputModel(),
+        input_info=FillerInputInfo([FillerInputElement([1, 1, 1, 1]), FillerInputElement([1, 1, 1, 1])]),
     )
     assert inspect.signature(another_nncf_net.forward) == inspect.signature(another_original_obj.forward)
     assert inspect.signature(simple_net.forward) == inspect.signature(original_obj.forward)