Update black formatting (latest library version)

Author: fkodom

doc/scripts/generate_benchmark_plot.py

@@ -24,7 +24,8 @@ def __str__(self):
 
 def benchmark(fn: Callable, *args, num_iterations: int = 10, **kwargs) -> Benchmark:
     timer = Timer(
-        "fn(*args, **kwargs)", globals={"fn": fn, "args": args, "kwargs": kwargs},
+        "fn(*args, **kwargs)",
+        globals={"fn": fn, "args": args, "kwargs": kwargs},
     )
     times = timer.repeat(number=1, repeat=num_iterations + 1)
     return Benchmark(np.mean(times[1:]).item(), np.std(times[1:]).item())

tests/test_functional.py

@@ -53,7 +53,12 @@ def test_fft_conv_functional(
     b0 = torch.randn(out_channels, requires_grad=True) if bias else None
     b1 = b0.detach().clone().requires_grad_() if bias else None
 
-    kwargs = dict(padding=padding, stride=stride, dilation=dilation, groups=groups,)
+    kwargs = dict(
+        padding=padding,
+        stride=stride,
+        dilation=dilation,
+        groups=groups,
+    )
 
     y0 = fft_conv(signal, w0, bias=b0, **kwargs)
     y1 = torch_conv(signal, w1, bias=b1, **kwargs)
@@ -99,7 +104,12 @@ def test_fft_conv_backward_functional(
     b0 = torch.randn(out_channels, requires_grad=True) if bias else None
     b1 = b0.detach().clone().requires_grad_() if bias else None
 
-    kwargs = dict(padding=padding, stride=stride, dilation=dilation, groups=groups,)
+    kwargs = dict(
+        padding=padding,
+        stride=stride,
+        dilation=dilation,
+        groups=groups,
+    )
 
     y0 = fft_conv(signal, w0, bias=b0, **kwargs)
     y1 = torch_conv(signal, w1, bias=b1, **kwargs)

tests/test_module.py

@@ -50,7 +50,12 @@ def test_fft_conv_module(
     weight = fft_conv_layer.weight
     bias = fft_conv_layer.bias
 
-    kwargs = dict(padding=padding, stride=stride, dilation=dilation, groups=groups,)
+    kwargs = dict(
+        padding=padding,
+        stride=stride,
+        dilation=dilation,
+        groups=groups,
+    )
 
     y0 = fft_conv_layer(signal)
     y1 = torch_conv(signal, weight, bias=bias, **kwargs)
@@ -102,7 +107,12 @@ def test_fft_conv_backward_module(
     b0 = fft_conv_layer.bias
     b1 = b0.detach().clone().requires_grad_() if bias else None
 
-    kwargs = dict(padding=padding, stride=stride, dilation=dilation, groups=groups,)
+    kwargs = dict(
+        padding=padding,
+        stride=stride,
+        dilation=dilation,
+        groups=groups,
+    )
 
     y0 = fft_conv_layer(signal)
     y1 = torch_conv(signal, w1, bias=b1, **kwargs)