Deciding number of layers for SVHN dataset

I am running FL setup on SVHN dataset and having error :RuntimeError: mat1 and mat2 shapes cannot be multiplied (32768x3 and 20480*2048
)
My sample input shape is : (32, 32, 3) with 10 outputs and I am running it on model:

class Net(nn.Module):
def init(self):
super(Net, self).init()
self.fc1 = nn.Linear(32,2048)
self.fc2 = nn.Linear(2048,64)
self.fc3 = nn.Linear(64,10)
self.dropout = nn.Dropout(0.10)
def forward(self, x):
x = self.fc1(x)
x = F.relu(x)
print(“x.shape”,x.shape)
x = self.dropout(x)
x = self.fc2(x)
x = F.relu(x)
x = self.fc3(x)
x = F.relu(x)

    output = F.log_softmax(x, dim=-1)
    return output

Can anyone please help me in deciding the neurons.

Double post from here.