format

.flake8

@@ -0,0 +1,3 @@
+[flake8]
+extend-ignore = F811,D1,D205,D209,D213,D400,D401,D999,D202,E203,E501,W503,E721,F403,F405,E701
+exclude = .git,__pycache__,docs,old,build,dis,tests/annotations/*, tests/test_py310.py,docs/*, src/mygrad/__init__.py, src/mygrad/numpy_compat/__init__.py,src/mygrad/nnet/__init__.py

src/mygrad/indexing_routines/funcs.py

@@ -17,7 +17,7 @@ def where(
     x: ArrayLike = _NoValue,
     y: ArrayLike = _NoValue,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     """
     where(condition, [x, y])

src/mygrad/linalg/ops.py

@@ -264,7 +264,7 @@ def __call__(
         axis=None,
         keepdims: bool = False,
         *,
-        nan_to_num: bool = True
+        nan_to_num: bool = True,
     ):
         self.variables = (tensor,)
         self._nan_to_num = nan_to_num

src/mygrad/math/sequential/funcs.py

@@ -36,7 +36,7 @@ def sum(
     axis: Axis = None,
     keepdims: bool = False,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     """
     Sum of tensor elements over a given axis.
@@ -124,7 +124,7 @@ def mean(
     axis: Axis = None,
     keepdims: bool = False,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     """
     Mean of tensor elements over a given axis.
@@ -202,7 +202,7 @@ def var(
     ddof: int = 0,
     keepdims: bool = False,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     """
     Compute the variance along the specified axis.
@@ -298,7 +298,7 @@ def std(
     ddof: int = 0,
     keepdims: bool = False,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     """
     Compute the standard deviation along the specified axis.
@@ -392,7 +392,7 @@ def max(
     axis: Axis = None,
     keepdims: bool = False,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     """
     Return the maximum of a tensor or maximum along its axes.
@@ -457,7 +457,7 @@ def min(
     axis: Axis = None,
     keepdims: bool = False,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     """
     Return the minimum of a tensor or minimum along its axes.
@@ -527,7 +527,7 @@ def prod(
     axis: Axis = None,
     keepdims: bool = False,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     """
     Return the product of array elements over given axes.

src/mygrad/nnet/activations/softmax.py

@@ -41,7 +41,7 @@ def softmax(
     x: ArrayLike,
     axis: Union[None, int, Tuple[int, ...]] = -1,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     r"""
     Applies the softmax activation function::
@@ -115,7 +115,7 @@ def logsoftmax(
     x: ArrayLike,
     axis: Union[None, int, Tuple[int, ...]] = -1,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     r"""
     Applies the log-softmax activation function::

src/mygrad/nnet/losses/margin_ranking_loss.py

@@ -55,7 +55,7 @@ def margin_ranking_loss(
     y: ArrayLike,
     margin: float,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     r"""Computes the margin average margin ranking loss.
     Equivalent to::

src/mygrad/nnet/losses/multiclass_hinge.py

@@ -64,7 +64,7 @@ def multiclass_hinge(
     y_true: ArrayLike,
     hinge: float = 1.0,
     *,
-    constant: Optional[bool] = None
+    constant: Optional[bool] = None,
 ) -> Tensor:
     """Computes the average multiclass hinge loss.