diff --git a/colossalai/fx/tracer/meta_patch/patched_module/convolution.py b/colossalai/fx/tracer/meta_patch/patched_module/convolution.py
index 327450cb2..32bf1b8da 100644
--- a/colossalai/fx/tracer/meta_patch/patched_module/convolution.py
+++ b/colossalai/fx/tracer/meta_patch/patched_module/convolution.py
@@ -56,33 +56,32 @@ def torch_nn_conv3d(self, input):
     )
     return torch.empty(result_shape, device='meta')
 
+
 @meta_patched_module.register(torch.nn.ConvTranspose1d)
 def torch_nn_convtranspose1d(self, input):
     # the output shape is calculated using the formula stated
     # at https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose1d.html
     l_in = input.shape[-1]
     c_out = self.out_channels
-    l_out = math.floor((l_in - 1) * self.stride[0] - 2 * self.padding[0] + 
-                       self.dilation[0] * (self.kernel_size[0] - 1) + 
-                       self.output_padding[0] + 1)
+    l_out = math.floor((l_in - 1) * self.stride[0] - 2 * self.padding[0] + self.dilation[0] *
+                       (self.kernel_size[0] - 1) + self.output_padding[0] + 1)
     result_shape = input.shape[:-2] + (
         c_out,
         l_out,
     )
     return torch.empty(result_shape, device='meta')
 
+
 @meta_patched_module.register(torch.nn.ConvTranspose2d)
 def torch_nn_convtranspose2d(self, input):
     # the output shape is calculated using the formula stated
     # at https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html
     h_in, w_in = input.shape[-2:]
     c_out = self.out_channels
-    h_out = math.floor((h_in - 1) * self.stride[0] - 2 * self.padding[0] +
-                       self.dilation[0] * (self.kernel_size[0] - 1) + 
-                       self.output_padding[0] + 1)
-    w_out = math.floor((w_in - 1) * self.stride[1] - 2 * self.padding[1] +
-                       self.dilation[1] * (self.kernel_size[1] - 1) + 
-                       self.output_padding[1] + 1)
+    h_out = math.floor((h_in - 1) * self.stride[0] - 2 * self.padding[0] + self.dilation[0] *
+                       (self.kernel_size[0] - 1) + self.output_padding[0] + 1)
+    w_out = math.floor((w_in - 1) * self.stride[1] - 2 * self.padding[1] + self.dilation[1] *
+                       (self.kernel_size[1] - 1) + self.output_padding[1] + 1)
     result_shape = input.shape[:-3] + (
         c_out,
         h_out,
@@ -90,25 +89,23 @@ def torch_nn_convtranspose2d(self, input):
     )
     return torch.empty(result_shape, device='meta')
 
+
 @meta_patched_module.register(torch.nn.ConvTranspose3d)
 def torch_nn_convtranspose3d(self, input):
     # the output shape is calculated using the formula stated
     # at https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose3d.html
     d_in, h_in, w_in = input.shape[-3:]
     c_out = self.out_channels
-    d_out = math.floor((d_in - 1) * self.stride[0] - 2 * self.padding[0] +
-                       self.dilation[0] * (self.kernel_size[0] - 1) + 
-                       self.output_padding[0] + 1)
-    h_out = math.floor((h_in - 1) * self.stride[1] - 2 * self.padding[1] +
-                       self.dilation[1] * (self.kernel_size[1] - 1) + 
-                       self.output_padding[1] + 1)
-    w_out = math.floor((w_in - 1) * self.stride[2] - 2 * self.padding[2] +
-                       self.dilation[2] * (self.kernel_size[2] - 1) + 
-                       self.output_padding[2] + 1)
+    d_out = math.floor((d_in - 1) * self.stride[0] - 2 * self.padding[0] + self.dilation[0] *
+                       (self.kernel_size[0] - 1) + self.output_padding[0] + 1)
+    h_out = math.floor((h_in - 1) * self.stride[1] - 2 * self.padding[1] + self.dilation[1] *
+                       (self.kernel_size[1] - 1) + self.output_padding[1] + 1)
+    w_out = math.floor((w_in - 1) * self.stride[2] - 2 * self.padding[2] + self.dilation[2] *
+                       (self.kernel_size[2] - 1) + self.output_padding[2] + 1)
     result_shape = input.shape[:-4] + (
         c_out,
         d_out,
         h_out,
         w_out,
     )
-    return torch.empty(result_shape, device='meta')
\ No newline at end of file
+    return torch.empty(result_shape, device='meta')