diff --git a/deeplake/api/dataset.py b/deeplake/api/dataset.py
index 45e96711af..5e42d70feb 100644
--- a/deeplake/api/dataset.py
+++ b/deeplake/api/dataset.py
@@ -72,6 +72,7 @@
     LockedException,
     BadRequestException,
     RenameError,
+    InvalidPandasDataframeError,
 )
 from deeplake.util.storage import (
     get_storage_and_cache_chain,
@@ -82,6 +83,17 @@
 from deeplake.util.cache_chain import generate_chain
 from deeplake.core.storage.deeplake_memory_object import DeepLakeMemoryObject
 
+allow_delete_error_message = "Dataset overwrite failed. The dataset is marked as allow_delete=false. To allow overwrite, you must first run `allow_delete = True` on the dataset."
+see_traceback_error_message = (
+    "Dataset overwrite failed. See traceback for more information."
+)
+
+dataset_corrupted_error_message = (
+    "The source dataset is corrupted."
+    "You can try to fix this by loading the dataset with `reset=True` "
+    "which will attempt to reset uncommitted HEAD changes and load the previous version."
+)
+
 
 def _check_indra_and_read_only_flags(indra: bool, read_only: Optional[bool]):
     if indra == False:
@@ -258,7 +270,7 @@ def init(
         db_engine = parse_runtime_parameters(path, runtime)["tensor_db"]
 
         try:
-            storage, cache_chain = get_storage_and_cache_chain(
+            _, cache_chain = get_storage_and_cache_chain(
                 path=path,
                 db_engine=db_engine,
                 read_only=read_only,
@@ -279,16 +291,12 @@ def init(
         if ds_exists:
             if overwrite:
                 if not dataset._allow_delete(cache_chain):
-                    raise DatasetHandlerError(
-                        "Dataset overwrite failed. The dataset is marked as allow_delete=false. To allow overwrite, you must first run `allow_delete = True` on the dataset."
-                    )
+                    raise DatasetHandlerError(allow_delete_error_message)
 
                 try:
                     cache_chain.clear()
                 except Exception as e:
-                    raise DatasetHandlerError(
-                        "Dataset overwrite failed. See traceback for more information."
-                    ) from e
+                    raise DatasetHandlerError(see_traceback_error_message) from e
                 create = True
             else:
                 create = False
@@ -403,7 +411,7 @@ def exists(
         _fetch_creds_from_key(creds, org_id, token)
 
         try:
-            storage, cache_chain = get_storage_and_cache_chain(
+            storage, _ = get_storage_and_cache_chain(
                 path=path,
                 read_only=True,
                 creds=creds,
@@ -523,16 +531,12 @@ def empty(
 
         if overwrite and dataset_exists(cache_chain):
             if not dataset._allow_delete(cache_chain):
-                raise DatasetHandlerError(
-                    "Dataset overwrite failed. The dataset is marked as allow_delete=false. To allow overwrite, you must first run `allow_delete = True` on the dataset."
-                )
+                raise DatasetHandlerError(allow_delete_error_message)
 
             try:
                 cache_chain.clear()
             except Exception as e:
-                raise DatasetHandlerError(
-                    "Dataset overwrite failed. See traceback for more information."
-                ) from e
+                raise DatasetHandlerError(see_traceback_error_message) from e
         elif dataset_exists(cache_chain):
             raise DatasetHandlerError(
                 f"A dataset already exists at the given path ({path}). If you want to create"
@@ -1143,11 +1147,11 @@ def _like(  # (No reporting)
                 if dest_path == src_path:
                     # load tensor data to memory before deleting
                     # in case of in-place deeplake.like
-                    meta = source_tensor.meta
-                    info = source_tensor.info
-                    sample_shape_tensor = source_tensor._sample_shape_tensor
-                    sample_id_tensor = source_tensor._sample_id_tensor
-                    sample_info_tensor = source_tensor._sample_info_tensor
+                    _ = source_tensor.meta
+                    _ = source_tensor.info
+                    _ = source_tensor._sample_shape_tensor
+                    _ = source_tensor._sample_id_tensor
+                    _ = source_tensor._sample_info_tensor
                 destination_ds.delete_tensor(tensor_name)
             destination_ds.create_tensor_like(tensor_name, source_tensor, unlink=tensor_name in unlink)  # type: ignore
 
@@ -1218,9 +1222,7 @@ def copy(
                 )
             except DatasetCorruptError as e:
                 raise DatasetCorruptError(
-                    "The source dataset is corrupted.",
-                    "You can try to fix this by loading the dataset with `reset=True` "
-                    "which will attempt to reset uncommitted HEAD changes and load the previous version.",
+                    dataset_corrupted_error_message,
                     e.__cause__,
                 )
         else:
@@ -1323,9 +1325,7 @@ def deepcopy(
                 )
             except DatasetCorruptError as e:
                 raise DatasetCorruptError(
-                    "The source dataset is corrupted.",
-                    "You can try to fix this by loading the dataset with `reset=True` "
-                    "which will attempt to reset uncommitted HEAD changes and load the previous version.",
+                    dataset_corrupted_error_message,
                     e.__cause__,
                 )
         else:
@@ -1364,16 +1364,12 @@ def deepcopy(
         if dataset_exists(cache_chain):
             if overwrite:
                 if not dataset._allow_delete(cache_chain):
-                    raise DatasetHandlerError(
-                        "Dataset overwrite failed. The dataset is marked as allow_delete=false. To allow overwrite, you must first run `allow_delete = True` on the dataset."
-                    )
+                    raise DatasetHandlerError(allow_delete_error_message)
 
                 try:
                     cache_chain.clear()
                 except Exception as e:
-                    raise DatasetHandlerError(
-                        "Dataset overwrite failed. See traceback for more information."
-                    ) from e
+                    raise DatasetHandlerError(see_traceback_error_message) from e
             else:
                 raise DatasetHandlerError(
                     f"A dataset already exists at the given path ({dest}). If you want to copy to a new dataset, either specify another path or use overwrite=True."
@@ -2125,7 +2121,7 @@ def ingest_dataframe(
             Dataset: New dataset created from the dataframe.
 
         Raises:
-            Exception: If ``src`` is not a valid pandas dataframe object.
+            InvalidPandasDataframeError: If ``src`` is not a valid pandas dataframe object.
         """
         import pandas as pd
         from deeplake.auto.structured.dataframe import DataFrame
@@ -2138,7 +2134,7 @@ def ingest_dataframe(
         )
 
         if not isinstance(src, pd.DataFrame):
-            raise Exception("Source provided is not a valid pandas dataframe object")
+            raise InvalidPandasDataframeError()
 
         structured = DataFrame(src, column_params, src_creds, creds_key)
 
@@ -2244,9 +2240,7 @@ def export_yolo(
                 )
             except DatasetCorruptError as e:
                 raise DatasetCorruptError(
-                    "The source dataset is corrupted.",
-                    "You can try to fix this by loading the dataset with `reset=True` "
-                    "which will attempt to reset uncommitted HEAD changes and load the previous version.",
+                    dataset_corrupted_error_message,
                     e.__cause__,
                 )
         else:
diff --git a/deeplake/auto/structured/dataframe.py b/deeplake/auto/structured/dataframe.py
index 1b892d066d..43330b865d 100644
--- a/deeplake/auto/structured/dataframe.py
+++ b/deeplake/auto/structured/dataframe.py
@@ -3,7 +3,7 @@
 from deeplake import Dataset
 from deeplake import read, link
 from deeplake.htype import HTYPE_SUPPORTED_COMPRESSIONS
-from deeplake.util.exceptions import IngestionError
+from deeplake.util.exceptions import IngestionError, InvalidPandasDataframeError
 from deeplake.util.dataset import sanitize_tensor_name
 
 from collections import defaultdict
@@ -26,14 +26,14 @@ def __init__(self, source, column_params=None, creds=None, creds_key=None):
 
 
         Raises:
-            Exception: If source is not a pandas dataframe object.
+            InvalidPandasDataframeError: If source is not a pandas dataframe object.
         """
 
         import pandas as pd  # type: ignore
 
         super().__init__(source)
         if not isinstance(self.source, pd.DataFrame):
-            raise Exception("Source is not a pandas dataframe object.")
+            raise InvalidPandasDataframeError()
 
         self.creds = creds
         self.creds_key = creds_key
diff --git a/deeplake/auto/tests/test_ingestion.py b/deeplake/auto/tests/test_ingestion.py
index 1bd1946b5f..62253ef6d7 100644
--- a/deeplake/auto/tests/test_ingestion.py
+++ b/deeplake/auto/tests/test_ingestion.py
@@ -6,6 +6,7 @@
     SamePathException,
     DatasetHandlerError,
     IngestionError,
+    InvalidPandasDataframeError,
 )
 import numpy as np
 import pytest
diff --git a/deeplake/client/utils.py b/deeplake/client/utils.py
index e78afb71db..54a63938d7 100644
--- a/deeplake/client/utils.py
+++ b/deeplake/client/utils.py
@@ -29,18 +29,6 @@
 BEST_RECALL = "best_recall@10"
 
 
-def remove_username_from_config():
-    try:
-        config = {}
-        with open(REPORTING_CONFIG_FILE_PATH, "r") as f:
-            config = json.load(f)
-            config["username"] = "public"
-        with open(REPORTING_CONFIG_FILE_PATH, "w") as f:
-            json.dump(config, f)
-    except (FileNotFoundError, KeyError):
-        return
-
-
 def check_response_status(response: requests.Response):
     """Check response status and throw corresponding exception on failure."""
     code = response.status_code
diff --git a/deeplake/core/chunk/chunk_compressed_chunk.py b/deeplake/core/chunk/chunk_compressed_chunk.py
index 8853c3b3c8..451f1dfb62 100644
--- a/deeplake/core/chunk/chunk_compressed_chunk.py
+++ b/deeplake/core/chunk/chunk_compressed_chunk.py
@@ -160,13 +160,12 @@ def extend_if_has_space_byte_compression_numpy(
             cast = False
             sample_nbytes = sample.nbytes
         else:
-            if sample.size:
-                if not np.can_cast(sample_dtype, chunk_dtype):
-                    raise TensorDtypeMismatchError(
-                        chunk_dtype,
-                        sample_dtype,
-                        self.htype,
-                    )
+            if sample.size and not np.can_cast(sample_dtype, chunk_dtype):
+                raise TensorDtypeMismatchError(
+                    chunk_dtype,
+                    sample_dtype,
+                    self.htype,
+                )
             cast = True
             sample_nbytes = np.dtype(chunk_dtype).itemsize * sample.size
         min_chunk_size = self.min_chunk_size
diff --git a/deeplake/core/chunk/uncompressed_chunk.py b/deeplake/core/chunk/uncompressed_chunk.py
index a30abab715..feac604558 100644
--- a/deeplake/core/chunk/uncompressed_chunk.py
+++ b/deeplake/core/chunk/uncompressed_chunk.py
@@ -48,9 +48,8 @@ def _extend_if_has_space_text(
         num_data_bytes = self.num_data_bytes
         space_left = min_chunk_size - num_data_bytes
         idx = np.searchsorted(csum, space_left)
-        if not idx and csum[0] > space_left:
-            if self._data_bytes:
-                return 0
+        if not idx and csum[0] > space_left and self._data_bytes:
+            return 0
         num_samples = int(min(len(incoming_samples), idx + 1))  # type: ignore
         bts = list(
             map(self._text_sample_to_byte_string, incoming_samples[:num_samples])
@@ -131,13 +130,12 @@ def _extend_if_has_space_numpy(
         chunk_dtype = self.dtype
         samples_dtype = incoming_samples.dtype
         if samples_dtype != chunk_dtype:
-            if size:
-                if not np.can_cast(samples_dtype, chunk_dtype):
-                    raise TensorDtypeMismatchError(
-                        chunk_dtype,
-                        samples_dtype,
-                        self.htype,
-                    )
+            if size and not np.can_cast(samples_dtype, chunk_dtype):
+                raise TensorDtypeMismatchError(
+                    chunk_dtype,
+                    samples_dtype,
+                    self.htype,
+                )
             samples = samples.astype(chunk_dtype)
         self._data_bytes += samples.tobytes()  # type: ignore
         self.register_in_meta_and_headers(
@@ -163,7 +161,7 @@ def _extend_if_has_space_list(
                 if shape is not None and not self.tensor_meta.is_link:
                     self.num_dims = self.num_dims or len(shape)
                     check_sample_shape(shape, self.num_dims)
-            except Exception as e:
+            except Exception:
                 if ignore_errors:
                     skipped.append(i)
                     continue
diff --git a/deeplake/core/chunk_engine.py b/deeplake/core/chunk_engine.py
index 963b8f8b56..9896723781 100644
--- a/deeplake/core/chunk_engine.py
+++ b/deeplake/core/chunk_engine.py
@@ -838,26 +838,27 @@ def _samples_to_chunks(
         incoming_num_samples = len(samples)
         enc_ids: List[Optional[str]] = []
         enc_count = [0]
-        if extending:
-            if self.tensor_meta.htype == "text" and (
-                self.chunk_class != SampleCompressedChunk
-            ):
-                lengths = np.zeros(len(samples), dtype=np.uint32)
-                for i, s in enumerate(samples):
-                    try:
-                        s = s.numpy()
-                    except AttributeError:
-                        pass
+        if (
+            extending
+            and self.tensor_meta.htype == "text"
+            and (self.chunk_class != SampleCompressedChunk)
+        ):
+            lengths = np.zeros(len(samples), dtype=np.uint32)
+            for i, s in enumerate(samples):
+                try:
+                    s = s.numpy()
+                except AttributeError:
+                    pass
+                try:
+                    if s.dtype.name[:3] == "str":
+                        lengths[i] = len(str(s.reshape(())))
+                except AttributeError:
                     try:
-                        if s.dtype.name[:3] == "str":
-                            lengths[i] = len(str(s.reshape(())))
-                    except AttributeError:
-                        try:
-                            lengths[i] = s.__len__()
-                        except AttributeError:  # None
-                            lengths[i] = 0
-                        except TypeError:  # Numpy scalar str
-                            lengths[i] = str(s).__len__()
+                        lengths[i] = s.__len__()
+                    except AttributeError:  # None
+                        lengths[i] = 0
+                    except TypeError:  # Numpy scalar str
+                        lengths[i] = str(s).__len__()
         extra_args = {"lengths": lengths}
         current_chunk = start_chunk
         updated_chunks: List[Optional[str]] = []
@@ -1039,12 +1040,11 @@ def _handle_tiled_sample(
         lengths,
     ):
         sample = samples[0]
-        if sample.is_first_write:
-            if register:
-                if start_chunk_row is not None:
-                    enc.register_samples(1)
-                else:
-                    enc_count[-1] += 1
+        if sample.is_first_write and register:
+            if start_chunk_row is not None:
+                enc.register_samples(1)
+            else:
+                enc_count[-1] += 1
         if sample.is_last_write:
             tiles[
                 incoming_num_samples - len(samples) + bool(register) * orig_meta_length
@@ -1775,7 +1775,7 @@ def _update_with_operator(
         samples: Union[np.ndarray, Sequence[InputSample], InputSample],
         operator: str,
     ):
-        """Update data at `index` with the output of elem-wise operatorion with samples"""
+        """Update data at `index` with the output of elem-wise operation with samples"""
         try:
             if isinstance(samples, deeplake.core.tensor.Tensor):
                 samples = samples.numpy()
@@ -1891,7 +1891,7 @@ def _get_full_chunk(self, index) -> bool:
         """
         threshold = 10
 
-        if type(index.values[0].value) == slice:
+        if isinstance(index.values[0].value, slice):
             start = index.values[0].value.start or 0
             stop = index.values[0].value.stop or self.num_samples
             step = index.values[0].value.step or 1
@@ -2218,11 +2218,10 @@ def load_chunks(
                     if exception:
                         raise exception
                     chunk, chunk_info = future.result()
-                    if chunk:
-                        if _get_nbytes(chunk) <= self.cache.cache_size:
-                            self.cache._insert_in_cache(
-                                self.get_chunk_key_for_id(chunk_info[0]), chunk
-                            )
+                    if chunk and _get_nbytes(chunk) <= self.cache.cache_size:
+                        self.cache._insert_in_cache(
+                            self.get_chunk_key_for_id(chunk_info[0]), chunk
+                        )
                     yield chunk_info
         else:
             with ThreadPoolExecutor() as executor:
@@ -2232,11 +2231,10 @@ def load_chunks(
                     repeat(storages),
                 ):
                     chunk, chunk_info = result
-                    if chunk:
-                        if _get_nbytes(chunk) <= self.cache.cache_size:
-                            self.cache._insert_in_cache(
-                                self.get_chunk_key_for_id(chunk_info[0]), chunk
-                            )
+                    if chunk and _get_nbytes(chunk) <= self.cache.cache_size:
+                        self.cache._insert_in_cache(
+                            self.get_chunk_key_for_id(chunk_info[0]), chunk
+                        )
                     yield chunk_info
 
     def _get_samples(
diff --git a/deeplake/core/tensor.py b/deeplake/core/tensor.py
index 90ade0cde7..962661ff1e 100644
--- a/deeplake/core/tensor.py
+++ b/deeplake/core/tensor.py
@@ -1546,11 +1546,11 @@ def deserialize_inverted_index(self, serialized_data):
         stream = BytesIO(serialized_data)
 
         # Read number of partitions
-        metadataSize = int.from_bytes(
+        metadata_size = int.from_bytes(
             stream.read(8), "little"
         )  # Assuming size_t is 8 bytes
 
-        metadata_bytes = stream.read(metadataSize)
+        metadata_bytes = stream.read(metadata_size)
         metadata = json.loads(metadata_bytes.decode("utf-8"))
 
         temp_paths_size = int.from_bytes(stream.read(8), "little")
@@ -1847,7 +1847,6 @@ def create_vdb_index(
                 metadata_json = json.dumps(metadata)
                 metadata_bytes = metadata_json.encode("utf-8")
                 self.storage[inverted_meta_key] = metadata_bytes
-                temp_serialized_paths_count = len(temp_serialized_paths)
                 temp_serialized_paths = [str(path) for path in temp_serialized_paths]
                 for i, path in enumerate(temp_serialized_paths):
                     file_name = pathlib.Path(path).name
@@ -1985,7 +1984,7 @@ def _verify_and_delete_vdb_indexes(self):
     def load_vdb_index(self, id: str):
         if not self.meta.contains_vdb_index(id):
             raise ValueError(f"Tensor meta has no vdb index with name '{id}'.")
-        if not self.dataset.libdeeplake_dataset is None:
+        if self.dataset.libdeeplake_dataset is not None:
             ds = self.dataset.libdeeplake_dataset
         else:
             from deeplake.enterprise.convert_to_libdeeplake import (
@@ -2003,7 +2002,7 @@ def load_vdb_index(self, id: str):
             raise ValueError(f"An error occurred while loading the VDB index {id}: {e}")
 
     def unload_vdb_index_cache(self):
-        if not self.dataset.libdeeplake_dataset is None:
+        if self.dataset.libdeeplake_dataset is not None:
             ds = self.dataset.libdeeplake_dataset
         else:
             from deeplake.enterprise.convert_to_libdeeplake import (
@@ -2028,7 +2027,7 @@ def get_vdb_indexes(self) -> List[Dict[str, str]]:
     def fetch_vdb_indexes(self) -> List[Dict[str, str]]:
         vdb_indexes = []
         if self.meta.htype == "embedding" or self.meta.htype == "text":
-            if (not self.meta.vdb_indexes is None) and len(self.meta.vdb_indexes) > 0:
+            if (self.meta.vdb_indexes is not None) and len(self.meta.vdb_indexes) > 0:
                 vdb_indexes.extend(self.meta.vdb_indexes)
         return vdb_indexes
 
diff --git a/deeplake/core/tests/test_indra_dataset.py b/deeplake/core/tests/test_indra_dataset.py
index 4b9c2459ed..9d578c49cb 100644
--- a/deeplake/core/tests/test_indra_dataset.py
+++ b/deeplake/core/tests/test_indra_dataset.py
@@ -17,7 +17,7 @@ def test_indexing(local_auth_ds_generator):
         deeplake_ds.create_tensor(
             "label", htype="generic", dtype=np.int32, **disabale_hidden_tensors_config
         )
-        for i in range(1000):
+        for _ in range(1000):
             deeplake_ds.label.append(int(100 * random.uniform(0.0, 1.0)))
 
     indra_ds = dataset_to_libdeeplake(deeplake_ds)
@@ -64,7 +64,7 @@ def test_save_view(local_auth_ds_generator):
         deeplake_ds.create_tensor(
             "label", htype="generic", dtype=np.int32, **disabale_hidden_tensors_config
         )
-        for i in range(1000):
+        for _ in range(1000):
             deeplake_ds.label.append(int(100 * random.uniform(0.0, 1.0)))
 
     deeplake_ds.commit("First")
@@ -104,7 +104,7 @@ def test_load_view(local_auth_ds_generator):
     deeplake_indra_ds = IndraDatasetView(indra_ds=indra_ds)
 
     with pytest.raises(Exception):
-        dataloader = deeplake_indra_ds.pytorch()
+        deeplake_indra_ds.pytorch()
 
     query_str = "select * group by label"
     view = deeplake_ds.query(query_str)
@@ -233,7 +233,7 @@ def test_accessing_data(local_auth_ds_generator):
         deeplake_ds.create_tensor(
             "label", htype="generic", dtype=np.int32, **disabale_hidden_tensors_config
         )
-        for i in range(1000):
+        for _ in range(1000):
             deeplake_ds.label.append(int(100 * random.uniform(0.0, 1.0)))
 
     indra_ds = dataset_to_libdeeplake(deeplake_ds)
diff --git a/deeplake/integrations/mmseg/mmseg_.py b/deeplake/integrations/mmseg/mmseg_.py
index ef1f0e2cb2..cf62af0fb5 100644
--- a/deeplake/integrations/mmseg/mmseg_.py
+++ b/deeplake/integrations/mmseg/mmseg_.py
@@ -164,6 +164,7 @@
 from typing import Callable, Optional, List, Dict
 from prettytable import PrettyTable  # type: ignore
 from PIL import Image  # type: ignore
+from deeplake.util.exceptions import ValidationDatasetMissingError
 
 from mmseg.core import DistEvalHook, EvalHook  # type: ignore
 from mmseg.core import build_optimizer
@@ -611,7 +612,6 @@ def _train_segmentor(
     eval_cfg = cfg.get("evaluation", {})
     dl_impl = cfg.get("deeplake_dataloader_type", "auto").lower()
 
-    # TODO: check whether dataset is actually supported by enterprise dataloader if c++
     if dl_impl == "auto":
         dl_impl = "c++" if indra_available() else "python"
     elif dl_impl == "cpp":
@@ -733,9 +733,8 @@ def _train_segmentor(
         cfg.get("momentum_config", None),
     )
 
-    if distributed:
-        if isinstance(runner, EpochBasedRunner):
-            runner.register_hook(DistSamplerSeedHook())
+    if distributed and isinstance(runner, EpochBasedRunner):
+        runner.register_hook(DistSamplerSeedHook())
 
     # register eval hooks
     if validate:
@@ -764,14 +763,8 @@ def _train_segmentor(
 
         if ds_val is None:
             cfg_ds_val = cfg.data.get("val")
-            if cfg_ds_val is None:
-                raise Exception(
-                    "Validation dataset is not specified even though validate = True. Please set validate = False or specify a validation dataset."
-                )
-            elif cfg_ds_val.get("deeplake_path") is None:
-                raise Exception(
-                    "Validation dataset is not specified even though validate = True. Please set validate = False or specify a validation dataset."
-                )
+            if cfg_ds_val is None or cfg_ds_val.get("deeplake_path") is None:
+                raise ValidationDatasetMissingError()
 
             ds_val = load_ds_from_cfg(cfg.data.val)
             ds_val_tensors = cfg.data.val.get("deeplake_tensors", {})
@@ -779,13 +772,11 @@ def _train_segmentor(
             cfg_data = cfg.data.val.get("deeplake_path")
             if cfg_data is not None:
                 always_warn(
-                    "A Deep Lake dataset was specified in the cfg as well as inthe dataset input to train_segmentor. The dataset input to train_segmentor will be used in the workflow."
+                    "A Deep Lake dataset was specified in the cfg as well as in the dataset input to train_segmentor. The dataset input to train_segmentor will be used in the workflow."
                 )
 
         if ds_val is None:
-            raise Exception(
-                "Validation dataset is not specified even though validate = True. Please set validate = False or specify a validation dataset."
-            )
+            raise ValidationDatasetMissingError()
 
         if ds_val_tensors:
             val_images_tensor = ds_val_tensors["img"]
@@ -871,8 +862,8 @@ def build_dataloader(
     dataset.CLASSES = classes
     pipeline = build_pipeline(pipeline)
     persistent_workers = train_loader_config.get("persistent_workers", False)
-    ignore_index = train_loader_config.get("ignore_index")
-    reduce_zero_label = train_loader_config.get("reduce_zero_label")
+    _ = train_loader_config.get("ignore_index")
+    _ = train_loader_config.get("reduce_zero_label")
     dist = train_loader_config["dist"]
     if dist and implementation == "python":
         raise NotImplementedError(
diff --git a/deeplake/util/exceptions.py b/deeplake/util/exceptions.py
index 0767ad1fb8..11fc7f1d7e 100644
--- a/deeplake/util/exceptions.py
+++ b/deeplake/util/exceptions.py
@@ -593,7 +593,7 @@ def __init__(self, tensor, sample=None):
 
             if print_item:
                 msg += str(sample) + " "
-            elif print_path:
+            else:
                 msg += f"at path '{sample.path}' "
         else:
             msg = f"Failed to append a sample "
@@ -897,6 +897,7 @@ def __init__(self):
             "`ds.extend({'image_tensor': samples, 'label_tensor': samples})` or you need to call `extend` method of the required tensor, "
             "like: `ds.image_tensor.extend(samples)`"
         )
+        super().__init__(message)
 
 
 class DatasetTooLargeToDelete(Exception):
@@ -1143,9 +1144,23 @@ def __init__(self):
         super().__init__(msg)
 
 
+class ValidationDatasetMissingError(Exception):
+    def __init__(self):
+        msg = (
+            "Validation dataset is not specified even though validate = True. "
+            "Please set validate = False or specify a validation dataset."
+        )
+        super().__init__(msg)
+
+
 class InvalidAuthContextError(Exception):
     def __init__(
         self,
         message: str = "Authentication failed due to invalid or insufficient configuration.",
     ):
         super().__init__(message)
+
+
+class InvalidPandasDataframeError(Exception):
+    def __init__(self):
+        super().__init__("Source provided is not a valid pandas dataframe object")
diff --git a/deeplake/util/version_control.py b/deeplake/util/version_control.py
index ebe371d6ef..9230e703e9 100644
--- a/deeplake/util/version_control.py
+++ b/deeplake/util/version_control.py
@@ -412,7 +412,6 @@ def delete_branch(
     storage = dataset.storage
     storage.check_readonly()
 
-    # storage = dataset.storage
     version_state = dataset.version_state
     if version_state["branch"] == branch_name:
         raise VersionControlError(
diff --git a/deeplake/util/video.py b/deeplake/util/video.py
index 29dce68920..70ebc2e2cd 100644
--- a/deeplake/util/video.py
+++ b/deeplake/util/video.py
@@ -35,7 +35,7 @@ def normalize_index(index, nframes):
             start, stop = stop + 1, start + 1
     elif isinstance(index, list):
         raise IndexError(
-            f"Cannot specify a list video frames. You must specify a range with an optional step such as [5:10] or [0:100:5]"
+            "Cannot specify a list video frames. You must specify a range with an optional step such as [5:10] or [0:100:5]"
         )
     else:
         raise IndexError(
diff --git a/pyproject.toml b/pyproject.toml
index 4b0daa3703..08c6acea73 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,6 +19,7 @@ numpy = [
     { version = "^1.24.0", python = "<3.12" },
     { version = "^1.26.0", python = ">=3.12" },
 ]
+botocore = "*"
 python = ">=3.8,<=3.13"
 pillow = "~10.4.0"
 boto3 = "*"
@@ -30,13 +31,24 @@ lz4 = "*"
 pyjwt = "*"
 pydantic = "*"
 libdeeplake = { version = "0.0.146", markers = "python_version >= '3.8' and sys_platform != 'win32'" }
-
 av = { version = ">=8.1.0", markers = "python_version >= '3.7' or sys_platform != 'win32'" }
 aioboto3 = { version = ">=10.4.0", markers = "python_version >= '3.7' and sys_platform != 'win32'" }
+nest_asyncio = { version="*", markers = "python_version >= '3.7' and sys_platform != 'win32'" }
+google-cloud-storage = { version = "~1.42.0", optional = true }
+google-auth = { version = "~2.0.1", optional = true }
+google-auth-oauthlib = { version = "~0.4.5", optional = true }
+google-api-python-client = { version = "~2.31.0", optional = true }
+oauth2client = { version = "~4.1.3", optional = true }
 
 [tool.poetry.extras]
-gcp = ["google-cloud-storage", "google-auth", "google-auth-oauthlib"]
-av = ["av"]
+audio = ["av"]
+video = ["av"]
+gcp = [
+    "google-cloud-storage",
+    "google-auth",
+    "google-auth-oauthlib"
+]
+azure = ["azure-cli", "azure-identity", "azure-storage-blob"]
 dicom = ["pydicom", "nibabel"]
 medical = ["pydicom", "nibabel"]
 visualizer = ["IPython", "flask"]
@@ -68,9 +80,10 @@ all = [
     "pyjwt"
 ]
 
+
 [build-system]
-requires = ["poetry-core>=1.0.0"]
-build-backend = "poetry.core.masonry.api"
+requires = ["poetry-core>=1.0.0", "setuptools>=42"]
+build-backend = "setuptools.build_meta"
 
 [tool.coverage.run]
 relative_files = true