Your custom layer contains the weigth parameter printed by this code snippet:
class CustomLayer(torch.nn.Module):
    def __init__(self, input_shape):
        super(CustomLayer,self).__init__()
        zeros = torch.zeros(input_shape)
        self.weights = torch.nn.Parameter(zeros)
        torch.nn.init.xavier_uniform_(self.weights)
    def forward(self, x):
        out= torch.tanh(self.weights)
        return out+x

class MimicAndFool(torch.nn.Module):
    def __init__(self,input_shape):
        super(MimicAndFool,self).__init__()
        self.custom = CustomLayer(input_shape)
    def forward(self,x):
        out = self.custom(x)
        #statement 2
        #statement 3...
        #statement n
        return out

layer = CustomLayer((1, 1))
print(dict(layer.named_parameters()))
> {'weights': Parameter containing:
tensor([[-0.7979]], requires_grad=True)}

module = MimicAndFool((1, 1))
print(dict(module.named_parameters()))
> {'custom.weights': Parameter containing:
tensor([[0.8589]], requires_grad=True)}

so I assume torchsummary might not return the right values for this custom module.