chore: lint

k3_addons/layers/attention/bam.py

@@ -74,12 +74,14 @@ def call(self, x):
         res = ops.broadcast_to(res, ops.shape(x))
         return res
 
-@k3_export(path='k3_addons.layers.BAMBlock')
+
+@k3_export(path="k3_addons.layers.BAMBlock")
 class BAMBlock(layers.Layer):
     """
     BAM: Bottleneck Attention Module [https://arxiv.org/pdf/1807.06514.pdf]
-    
+
     """
+
     def __init__(self, reduction=16, dilation_rate=2):
         super().__init__()
         self.channel_attention = ChannelAttention(reduction=reduction)

k3_addons/layers/pooling/adaptive_pooling.py

@@ -112,9 +112,8 @@ def __init__(self, output_size, data_format=None, padding="valid", **kwargs):
 
 @k3_export(path="k3_addons.layers.AdaptiveAveragePool1D")
 class AdaptiveAveragePool1D(BaseAdaptivePool):
-    """ Adaptive Pooling like torch.nn.AdaptiveAvgPool1d
-    
-    """
+    """Adaptive Pooling like torch.nn.AdaptiveAvgPool1d"""
+
     def __init__(self, output_size, data_format=None, padding="valid", **kwargs):
         super(AdaptiveAveragePool1D, self).__init__(
             output_size,
@@ -128,9 +127,8 @@ def __init__(self, output_size, data_format=None, padding="valid", **kwargs):
 
 @k3_export(path="k3_addons.layers.AdaptiveMaxPool2D")
 class AdaptiveMaxPool2D(BaseAdaptivePool):
-    """ Adaptive Pooling like torch.nn.AdaptiveMaxPool2d
-    
-    """
+    """Adaptive Pooling like torch.nn.AdaptiveMaxPool2d"""
+
     def __init__(self, output_size, data_format=None, padding="valid", **kwargs):
         super(AdaptiveMaxPool2D, self).__init__(
             output_size,
@@ -144,9 +142,8 @@ def __init__(self, output_size, data_format=None, padding="valid", **kwargs):
 
 @k3_export(path="k3_addons.layers.AdaptiveAveragePool2D")
 class AdaptiveAveragePool2D(BaseAdaptivePool):
-    """ Adaptive Pooling like torch.nn.AdaptiveAvgPool2d
-    
-    """
+    """Adaptive Pooling like torch.nn.AdaptiveAvgPool2d"""
+
     def __init__(self, output_size, data_format=None, padding="valid", **kwargs):
         super(AdaptiveAveragePool2D, self).__init__(
             output_size,