diff --git a/cranberry/features/datasets.py b/cranberry/features/datasets.py
index 3d79b15..30609b4 100644
--- a/cranberry/features/datasets.py
+++ b/cranberry/features/datasets.py
@@ -12,9 +12,7 @@
 from tqdm import tqdm
 from cranberry import Tensor
 
-_cache_dir: str = getenv(
-    "XDG_CACHE_HOME", os.path.expanduser("~/Library/Caches" if OSX else "~/.cache")
-)
+_cache_dir: str = getenv("XDG_CACHE_HOME", os.path.expanduser("~/Library/Caches" if OSX else "~/.cache"))
 
 
 def fetch(
@@ -43,9 +41,7 @@ def fetch(
                     progress_bar.update(f.write(chunk))
                 f.close()
                 if (file_size := os.stat(f.name).st_size) < total_length:
-                    raise RuntimeError(
-                        f"fetch size incomplete, {file_size} < {total_length}"
-                    )
+                    raise RuntimeError(f"fetch size incomplete, {file_size} < {total_length}")
                 pathlib.Path(f.name).rename(fp)
     return fp
 
@@ -53,9 +49,7 @@ def fetch(
 def _fetch_mnist(file, offset):
     return Tensor(
         np.frombuffer(
-            gzip.open(
-                fetch("https://storage.googleapis.com/cvdf-datasets/mnist/" + file)
-            ).read()[offset:],
+            gzip.open(fetch("https://storage.googleapis.com/cvdf-datasets/mnist/" + file)).read()[offset:],
             dtype=np.uint8,
         )
     )
diff --git a/cranberry/nn/__init__.py b/cranberry/nn/__init__.py
index c2bfdda..36b93d6 100644
--- a/cranberry/nn/__init__.py
+++ b/cranberry/nn/__init__.py
@@ -26,13 +26,9 @@ def parameters(self) -> List[Tensor]:
 class Linear(Module):
     # https://github.com/tinygrad/tinygrad/blob/master/tinygrad/nn/__init__.py#L72-L80
     def __init__(self, in_features: int, out_features: int, bias=True):
-        self.weight = Tensor.kaiming_uniform(
-            shape=(out_features, in_features), a=math.sqrt(5)
-        )
+        self.weight = Tensor.kaiming_uniform(shape=(out_features, in_features), a=math.sqrt(5))
         bound = 1 / math.sqrt(in_features)
-        self.bias = (
-            Tensor.uniform(out_features, low=-bound, high=bound) if bias else None
-        )
+        self.bias = Tensor.uniform(out_features, low=-bound, high=bound) if bias else None
 
     def __call__(self, x: Tensor) -> Tensor:
         return x.linear(weight=self.weight.transpose(0, 1), bias=self.bias)
diff --git a/cranberry/tensor.py b/cranberry/tensor.py
index d36b36c..519258a 100644
--- a/cranberry/tensor.py
+++ b/cranberry/tensor.py
@@ -60,12 +60,8 @@ def __init__(
         else:
             raise ValueError(f"Invalid data type {type(data)}")
 
-        assert self._shape == Shape(
-            self._data.shape
-        ), f"shape {self._shape} must match data shape {self._data.shape}"
-        assert self._shape == Shape(
-            self._grad.shape
-        ), f"shape {self._shape} must match grad shape {self._grad.shape}"
+        assert self._shape == Shape(self._data.shape), f"shape {self._shape} must match data shape {self._data.shape}"
+        assert self._shape == Shape(self._grad.shape), f"shape {self._shape} must match grad shape {self._grad.shape}"
 
         # self._requires_grad
         self._requires_grad: bool = requires_grad
@@ -81,12 +77,8 @@ def __init__(
     # ********************************************************
 
     def backward(self):
-        assert (
-            self._requires_grad
-        ), "cannot call backward on a tensor that doesn't require gradients"
-        assert (
-            self.shape == tuple()
-        ), f"backward can only be called for scalar tensors, but it has shape {self.shape})"
+        assert self._requires_grad, "cannot call backward on a tensor that doesn't require gradients"
+        assert self.shape == tuple(), f"backward can only be called for scalar tensors, but it has shape {self.shape})"
 
         topo = []
         visited = set()
@@ -127,9 +119,7 @@ def _broadcasted(self, other: Tensor) -> Tuple[Tensor, Tensor]:
     # ********************************************************
 
     def _unary_op(self, op: UnaryOps) -> Tensor:
-        out = Tensor._dummy(
-            shape=self._shape, requires_grad=self.requires_grad, prev=(self,), op=op
-        )
+        out = Tensor._dummy(shape=self._shape, requires_grad=self.requires_grad, prev=(self,), op=op)
         if op == UnaryOps.NEG:
             out._data -= self._data
 
@@ -197,19 +187,13 @@ def tanh(self) -> Tensor:
     # TODO: gelu sanity check
 
     def gelu(self) -> Tensor:
-        return (
-            0.5
-            * self
-            * (1 + (self * 0.7978845608 * (1 + 0.044715 * self * self)).tanh())
-        )
+        return 0.5 * self * (1 + (self * 0.7978845608 * (1 + 0.044715 * self * self)).tanh())
 
     # ********************************************************
     # ***************        binary ops        ***************
     # ********************************************************
 
-    def _binary_op(
-        self, other: Union[Tensor, int, float], reverse: bool, op: BinaryOps
-    ) -> Tensor:
+    def _binary_op(self, other: Union[Tensor, int, float], reverse: bool, op: BinaryOps) -> Tensor:
         if isinstance(other, (int, float)):
             other = Tensor(other)
         self, other = self._broadcasted(other)
@@ -301,26 +285,18 @@ def __rtruediv__(self, other) -> Tensor:
     # ********************************************************
 
     def _reduce_op(self, *args, op: ReduceOps) -> Tensor:
-        out = Tensor._dummy(
-            shape=Shape(()), requires_grad=self.requires_grad, prev=(self,), op=op
-        )
+        out = Tensor._dummy(shape=Shape(()), requires_grad=self.requires_grad, prev=(self,), op=op)
         if op == ReduceOps.SUM:
             dim, keepdim = args
             out._data = self._data.sum(axis=dim, keepdims=keepdim)
             out._grad = np.zeros_like(out._data)
-            out._shape = (
-                Shape(out._data.shape)
-                if isinstance(out._data, np.ndarray)
-                else Shape(())
-            )
+            out._shape = Shape(out._data.shape) if isinstance(out._data, np.ndarray) else Shape(())
 
             def backward():
                 if dim is None or keepdim:
                     self._grad += out._grad
                 else:
-                    o_new_shape = tuple(
-                        1 if i == dim else s for i, s in enumerate(self.shape)
-                    )
+                    o_new_shape = tuple(1 if i == dim else s for i, s in enumerate(self.shape))
                     self._grad += out._grad.reshape(o_new_shape)
 
             out._backward = backward
@@ -328,22 +304,14 @@ def backward():
             dim, keepdim = args
             out._data = self._data.max(axis=dim, keepdims=keepdim)
             out._grad = np.zeros_like(out._data)
-            out._shape = (
-                Shape(out._data.shape)
-                if isinstance(out._data, np.ndarray)
-                else Shape(())
-            )
+            out._shape = Shape(out._data.shape) if isinstance(out._data, np.ndarray) else Shape(())
 
             def backward():
                 if dim is None or keepdim:
                     self._grad += (self._data == out._data) * out._grad
                 else:
-                    o_new_shape = tuple(
-                        1 if i == dim else s for i, s in enumerate(self.shape)
-                    )
-                    self._grad += (
-                        self._data == out._data.reshape(o_new_shape)
-                    ) * out._grad.reshape(o_new_shape)
+                    o_new_shape = tuple(1 if i == dim else s for i, s in enumerate(self.shape))
+                    self._grad += (self._data == out._data.reshape(o_new_shape)) * out._grad.reshape(o_new_shape)
 
             out._backward = backward
         else:
@@ -390,9 +358,7 @@ def log_softmax(self, dim: int = -1) -> Tensor:
     # ********************************************************
 
     def _movement_op(self, *args, op: MovementOps) -> Tensor:
-        out = Tensor._dummy(
-            shape=Shape(args[0]), requires_grad=self.requires_grad, prev=(self,), op=op
-        )
+        out = Tensor._dummy(shape=Shape(args[0]), requires_grad=self.requires_grad, prev=(self,), op=op)
         if op == MovementOps.RESHAPE:
             out._data = self._data.reshape(args[0])
             out._grad = self._grad.reshape(args[0])
@@ -413,9 +379,7 @@ def backward():
                 while len(s_shape) < len(o_shape):
                     s_shape = (1,) + s_shape
                 axis = tuple(i for i in range(len(o_shape)) if s_shape[i] == 1)
-                self._grad += out._grad.sum(axis=axis, keepdims=True).reshape(
-                    self.shape
-                )
+                self._grad += out._grad.sum(axis=axis, keepdims=True).reshape(self.shape)
 
             out._backward = backward
         elif op == MovementOps.PERMUTE:
@@ -427,27 +391,21 @@ def backward():
 
     def reshape(self, *shape: int) -> Tensor:
         assert shape.count(-1) <= 1, "can only specify one unknown dimension"
-        assert all(
-            s > 0 or s == -1 for s in shape
-        ), "shape dimensions must be positive or -1"
+        assert all(s > 0 or s == -1 for s in shape), "shape dimensions must be positive or -1"
 
         if shape.count(-1) == 1:
             assert (
                 prod(self._shape) % -prod(shape) == 0
             ), f"cannot reshape tensor of size {prod(self._shape)} into shape {shape}"
-            shape = tuple(
-                s if s != -1 else prod(self._shape) // -prod(shape) for s in shape
-            )
-        assert prod(shape) == prod(
-            self._shape
-        ), f"cannot reshape tensor of size {prod(self._shape)} into shape {shape}"
+            shape = tuple(s if s != -1 else prod(self._shape) // -prod(shape) for s in shape)
+        assert prod(shape) == prod(self._shape), f"cannot reshape tensor of size {prod(self._shape)} into shape {shape}"
 
         return self._movement_op(shape, op=MovementOps.RESHAPE)
 
     # https://pytorch.org/docs/stable/generated/torch.Tensor.expand.html
     def expand(self, *shape: int) -> Tensor:
-        assert (
-            len(shape) >= len(self.shape)
+        assert len(shape) >= len(
+            self.shape
         ), f"the expanded shape {shape} must have at least as many dimensions as the original shape {self.shape}"
         assert all(
             s == 1 or s == e for s, e in zip(self.shape, shape[-len(self.shape) :])
@@ -465,16 +423,8 @@ def permute(self, *dims: int) -> Tensor:
     def flatten(self, start_dim: int = 0, end_dim: int = -1) -> Tensor:
         if end_dim == -1:
             end_dim = len(self.shape)
-        assert (
-            0 <= start_dim < end_dim <= len(self.shape)
-        ), "invalid start_dim or end_dim"
-        return self.reshape(
-            *(
-                self.shape[:start_dim]
-                + (prod(self.shape[start_dim:end_dim]),)
-                + self.shape[end_dim:]
-            )
-        )
+        assert 0 <= start_dim < end_dim <= len(self.shape), "invalid start_dim or end_dim"
+        return self.reshape(*(self.shape[:start_dim] + (prod(self.shape[start_dim:end_dim]),) + self.shape[end_dim:]))
 
     def transpose(self, dim1: int, dim2: int) -> Tensor:
         dims = list(range(len(self.shape)))
@@ -492,9 +442,7 @@ def matmul_2d(self, other: Tensor) -> Tensor:
         assert (
             len(self.shape) == 2 and len(other.shape) == 2
         ), "matmul_2d only supports 2D tensors, but got shapes {self.shape} and {other.shape}"
-        assert (
-            self.shape[1] == other.shape[0]
-        ), f"matmul_2d shape mismatch: {self.shape} and {other.shape}"
+        assert self.shape[1] == other.shape[0], f"matmul_2d shape mismatch: {self.shape} and {other.shape}"
         N, M, K = self.shape[0], self.shape[1], other.shape[1]
         return (self.reshape(N, 1, M) * other.permute(1, 0).reshape(1, K, M)).sum(dim=2)
 
@@ -502,9 +450,7 @@ def matmul(self, other: Tensor) -> Tensor:
         # https://pytorch.org/docs/stable/generated/torch.matmul.html
         # if both tensors are 1-dimensional, the dot product (scalar) is returned
         if len(self.shape) == 1 and len(other.shape) == 1:
-            assert (
-                self.shape[0] == other.shape[0]
-            ), f"matmul shape mismatch: {self.shape} and {other.shape}"
+            assert self.shape[0] == other.shape[0], f"matmul shape mismatch: {self.shape} and {other.shape}"
             return self.mul(other).sum()
         # if both arguments are 2-dimensional, the matrix-matrix product is returned
         elif len(self.shape) == 2 and len(other.shape) == 2:
@@ -521,14 +467,8 @@ def matmul(self, other: Tensor) -> Tensor:
         # if the first argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after
         # if the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after
         # the non-matrix (i.e. batch) dimensions are broadcasted (and thus must be broadcastable)
-        elif (
-            len(self.shape) >= 1
-            and len(other.shape) >= 1
-            and (len(self.shape) > 2 or len(other.shape) > 2)
-        ):
-            raise NotImplementedError(
-                "batched matrix multiply is not implemented yet: {self.shape} and {other.shape}"
-            )
+        elif len(self.shape) >= 1 and len(other.shape) >= 1 and (len(self.shape) > 2 or len(other.shape) > 2):
+            raise NotImplementedError("batched matrix multiply is not implemented yet: {self.shape} and {other.shape}")
         else:
             raise RuntimeError(f"Invalid matmul shapes {self.shape} and {other.shape}")
 
@@ -557,9 +497,7 @@ def sparse_categorical_crossentropy(self, Y: Tensor) -> Tensor:
         Y_pred = self.log_softmax()
         # TODO: need more efficient implementation. currently, it's not possible to use Y as a tensor of indices
         Y_onehot_data = np.zeros_like(Y_pred._data)
-        Y_onehot_data[
-            np.arange(prod(Y._data.shape)), (Y._data + 1e-5).astype(np.int32)
-        ] = 1
+        Y_onehot_data[np.arange(prod(Y._data.shape)), (Y._data + 1e-5).astype(np.int32)] = 1
         Y_onehot = Tensor(Y_onehot_data)
         return -(Y_onehot * Y_pred).sum() / prod(Y._data.shape)  # reduction="mean"
 
@@ -606,9 +544,7 @@ def uniform(shape: Union[Tuple[int, ...], int], low=0.0, high=1.0) -> Tensor:
     def kaiming_uniform(shape: Union[Tuple[int, ...], int], a: float = 0.01) -> Tensor:
         if isinstance(shape, int):
             shape = (shape,)
-        bound = (
-            math.sqrt(3.0) * math.sqrt(2.0 / (1 + a**2)) / math.sqrt(prod(shape[1:]))
-        )
+        bound = math.sqrt(3.0) * math.sqrt(2.0 / (1 + a**2)) / math.sqrt(prod(shape[1:]))
         return Tensor.uniform(shape, low=-bound, high=bound)
 
     # ********************************************************
@@ -616,9 +552,7 @@ def kaiming_uniform(shape: Union[Tuple[int, ...], int], a: float = 0.01) -> Tens
     # ********************************************************
 
     @staticmethod
-    def _dummy(
-        shape: Shape, requires_grad: bool, prev: Optional[Tuple[Tensor, ...]], op: Op
-    ) -> Tensor:
+    def _dummy(shape: Shape, requires_grad: bool, prev: Optional[Tuple[Tensor, ...]], op: Op) -> Tensor:
         return Tensor(
             data=np.zeros(shape.dims),
             shape=shape,
@@ -648,9 +582,7 @@ def ones(shape: Tuple[int], requires_grad: bool = False) -> Tensor:
         )
 
     def detach(self) -> Tensor:
-        return Tensor(
-            data=self._data, shape=self._shape, requires_grad=False, prev=None, op=None
-        )
+        return Tensor(data=self._data, shape=self._shape, requires_grad=False, prev=None, op=None)
 
     def numpy(self) -> np.ndarray:
         return self._data
@@ -679,9 +611,7 @@ def size(self, dim: Optional[int] = None):
         return self.shape if dim is None else self.shape[dim]
 
     def item(self) -> float:
-        assert (
-            self._shape == ()
-        ), f"item() only supports tensors with a single element, but got shape {self.shape}"
+        assert self._shape == (), f"item() only supports tensors with a single element, but got shape {self.shape}"
         return self._data.item()
 
     def __hash__(self):
diff --git a/examples/mnist.py b/examples/mnist.py
index ca34ab8..433027f 100644
--- a/examples/mnist.py
+++ b/examples/mnist.py
@@ -7,9 +7,7 @@
 X_train, Y_train, X_test, Y_test = mnist()
 X_train, X_test = X_train.flatten(1), X_test.flatten(1)
 
-model = nn.Sequential(
-    nn.Linear(784, 128), nn.ReLU(), nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 10)
-)
+model = nn.Sequential(nn.Linear(784, 128), nn.ReLU(), nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 10))
 
 optimizer = optim.SGD(model.parameters(), lr=0.001)  # TODO: use Adam
 
diff --git a/poetry.lock b/poetry.lock
index 0015a76..84704d6 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -181,24 +181,24 @@ files = [
 
 [[package]]
 name = "maturin"
-version = "1.7.0"
+version = "1.7.1"
 description = "Build and publish crates with pyo3, cffi and uniffi bindings as well as rust binaries as python packages"
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "maturin-1.7.0-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:87a1fae70f1a6ad694832c735abf9f010edc4971c5cf89d2e7a54651a1a3792a"},
-    {file = "maturin-1.7.0-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:6fd312c56846d3cafa7c45e362d96b526170e79b9adb5b8ea02a10c88906069c"},
-    {file = "maturin-1.7.0-py3-none-manylinux_2_12_i686.manylinux2010_i686.musllinux_1_1_i686.whl", hash = "sha256:928b82ceba924b1642c53f6684271e814b5ce5049cb4d35ff36bed078837eb83"},
-    {file = "maturin-1.7.0-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.musllinux_1_1_x86_64.whl", hash = "sha256:7460122333971b2492154c102d2981ae337ae0486dde7f4df7e645d724de59a5"},
-    {file = "maturin-1.7.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.musllinux_1_1_aarch64.whl", hash = "sha256:1f521ebe0344db8260df0d12779aefc06c1f763cd654151cf4a238fe14f65dc1"},
-    {file = "maturin-1.7.0-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.musllinux_1_1_armv7l.whl", hash = "sha256:0af4f2a4cfb99206d414dec138dd3aac3f506eb8928b7e38dfac570461b393d6"},
-    {file = "maturin-1.7.0-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.musllinux_1_1_ppc64le.whl", hash = "sha256:29187d5c3e1e166c14eaadc63a8adc25b6bbb3e5b055d1bc87f6ca92b4b6e331"},
-    {file = "maturin-1.7.0-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e9cd5b992b6c131c5f47c85e7bc266bf5bf94f29720856678431ce6c91b726df"},
-    {file = "maturin-1.7.0-py3-none-win32.whl", hash = "sha256:c1ae0b4162fb1152aea83098bf1b66a7bf6dd73fd1b108e6c4e22160118a997c"},
-    {file = "maturin-1.7.0-py3-none-win_amd64.whl", hash = "sha256:2bd8227e020a9308c076253f29224c53b08b2a4ed41fcd94b4eb9349684fcfe7"},
-    {file = "maturin-1.7.0-py3-none-win_arm64.whl", hash = "sha256:7c05226547778f31b73d48a19d11f57792bcc44f4047b84c73ea66cae2e62473"},
-    {file = "maturin-1.7.0.tar.gz", hash = "sha256:1ba5277dd7832dc6181d69a005182b97b3520945825058484ffd9296f2efb59c"},
+    {file = "maturin-1.7.1-py3-none-linux_armv6l.whl", hash = "sha256:372a141b31ae7396728d2dedc6061fe4522c1803ae1c05700d37008e1d1a2cc9"},
+    {file = "maturin-1.7.1-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:49939608095d9bcdf19d081dfd6ac1e8f915c645115090514c7b86e1e382f241"},
+    {file = "maturin-1.7.1-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:973126a36cfb9861b3207df579678c1bcd7c348578a41ccfbe80d811a84f1740"},
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+    {file = "maturin-1.7.1-py3-none-win32.whl", hash = "sha256:07c8800603e551a45e16fe7ad1742977097ea43c18b28e491df74d4ca15c5857"},
+    {file = "maturin-1.7.1-py3-none-win_amd64.whl", hash = "sha256:c5e7e6d130072ca76956106daa276f24a66c3407cfe6cf64c196d4299fd4175c"},
+    {file = "maturin-1.7.1-py3-none-win_arm64.whl", hash = "sha256:acf9f539f53a7ad64d406a40b27b768f67d75e6e4e93cb04b29025144a74ef45"},
+    {file = "maturin-1.7.1.tar.gz", hash = "sha256:147754cb3d81177ee12d9baf575d93549e76121dacd3544ad6a50ab718de2b9c"},
 ]
 
 [package.extras]
@@ -253,63 +253,64 @@ files = [
 
 [[package]]
 name = "numpy"
-version = "2.1.0"
+version = "2.1.1"
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+    {file = "numpy-2.1.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e8dfa9e94fc127c40979c3eacbae1e61fda4fe71d84869cc129e2721973231ef"},
+    {file = "numpy-2.1.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:910b47a6d0635ec1bd53b88f86120a52bf56dcc27b51f18c7b4a2e2224c29f0f"},
+    {file = "numpy-2.1.1-cp313-cp313t-musllinux_1_1_x86_64.whl", hash = "sha256:13cc11c00000848702322af4de0147ced365c81d66053a67c2e962a485b3717c"},
+    {file = "numpy-2.1.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:53e27293b3a2b661c03f79aa51c3987492bd4641ef933e366e0f9f6c9bf257ec"},
+    {file = "numpy-2.1.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:7be6a07520b88214ea85d8ac8b7d6d8a1839b0b5cb87412ac9f49fa934eb15d5"},
+    {file = "numpy-2.1.1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl", hash = "sha256:52ac2e48f5ad847cd43c4755520a2317f3380213493b9d8a4c5e37f3b87df504"},
+    {file = "numpy-2.1.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:50a95ca3560a6058d6ea91d4629a83a897ee27c00630aed9d933dff191f170cd"},
+    {file = "numpy-2.1.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:99f4a9ee60eed1385a86e82288971a51e71df052ed0b2900ed30bc840c0f2e39"},
+    {file = "numpy-2.1.1.tar.gz", hash = "sha256:d0cf7d55b1051387807405b3898efafa862997b4cba8aa5dbe657be794afeafd"},
 ]
 
 [[package]]
@@ -435,14 +436,14 @@ files = [
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.6.20"
+version = "12.6.68"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_aarch64.whl", hash = "sha256:84fb38465a5bc7c70cbc320cfd0963eb302ee25a5e939e9f512bbba55b6072fb"},
-    {file = "nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl", hash = "sha256:562ab97ea2c23164823b2a89cb328d01d45cb99634b8c65fe7cd60d14562bd79"},
-    {file = "nvidia_nvjitlink_cu12-12.6.20-py3-none-win_amd64.whl", hash = "sha256:ed3c43a17f37b0c922a919203d2d36cbef24d41cc3e6b625182f8b58203644f6"},
+    {file = "nvidia_nvjitlink_cu12-12.6.68-py3-none-manylinux2014_aarch64.whl", hash = "sha256:b3fd0779845f68b92063ab1393abab1ed0a23412fc520df79a8190d098b5cd6b"},
+    {file = "nvidia_nvjitlink_cu12-12.6.68-py3-none-manylinux2014_x86_64.whl", hash = "sha256:125a6c2a44e96386dda634e13d944e60b07a0402d391a070e8fb4104b34ea1ab"},
+    {file = "nvidia_nvjitlink_cu12-12.6.68-py3-none-win_amd64.whl", hash = "sha256:a55744c98d70317c5e23db14866a8cc2b733f7324509e941fc96276f9f37801d"},
 ]
 
 [[package]]
@@ -513,13 +514,13 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
 
 [[package]]
 name = "pyright"
-version = "1.1.377"
+version = "1.1.379"
 description = "Command line wrapper for pyright"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pyright-1.1.377-py3-none-any.whl", hash = "sha256:af0dd2b6b636c383a6569a083f8c5a8748ae4dcde5df7914b3f3f267e14dd162"},
-    {file = "pyright-1.1.377.tar.gz", hash = "sha256:aabc30fedce0ded34baa0c49b24f10e68f4bfc8f68ae7f3d175c4b0f256b4fcf"},
+    {file = "pyright-1.1.379-py3-none-any.whl", hash = "sha256:01954811ac71db8646f50de1577576dc275ffb891a9e7324350e676cf6df323f"},
+    {file = "pyright-1.1.379.tar.gz", hash = "sha256:6f426cb6443786fa966b930c23ad1941c8cb9fe672e4589daea8d80bb34193ea"},
 ]
 
 [package.dependencies]
diff --git a/pyproject.toml b/pyproject.toml
index d5ff7b8..656de90 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,6 +19,9 @@ pytest = "^8.3.1"
 torch = "^2.3.1"
 maturin = "^1.7.0"
 
+[tool.ruff]
+line-length = 120
+
 [build-system]
 requires = ["maturin>=0.13"]
 build-backend = "maturin"
diff --git a/tests/test_tensor.py b/tests/test_tensor.py
index 5072976..95bb37a 100644
--- a/tests/test_tensor.py
+++ b/tests/test_tensor.py
@@ -680,9 +680,7 @@ def test_pytorch():
             A = torch.tensor(A_np, requires_grad=True)
             W = torch.tensor(C_np)
             b = torch.tensor(E_np)
-            out = torch.nn.functional.linear(
-                A, W.transpose(1, 0), b
-            ).sum()  # transpose to match cranberry
+            out = torch.nn.functional.linear(A, W.transpose(1, 0), b).sum()  # transpose to match cranberry
             out.backward()
             assert A.grad is not None
             return out.detach().numpy(), A.grad.detach().numpy()