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@variational_estimator
class BNN(nn.Module):
    def __init__(self):
        super().__init__()
        # convolutional layer (sees 32x32x3 image tensor)
        self.conv1 = BayesianConv2d(3, 8, (3,3), padding=1)
        # convolutional layer (sees 16x16x8 tensor)
        self.conv2 = BayesianConv2d(8, 16, (3,3), padding=1)
        # convolutional layer (sees 8x8x16 tensor)
        self.conv3 = BayesianConv2d(16, 16, (3,3), padding=1)

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##############
# QUESTION 1: 

# a) replicating figure 9.7 (right panel) from Gelman et. al. 

# separating treatment and control data
foo = read.csv("https://tinyurl.com/wlgl63b")
ctrl_units = foo[which(foo$treatment==0),]
trt_units = foo[which(foo$treatment==1),]

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