The output of  the second max pooling layer will have a shape of [batch_size, 16 ,5 ,5], so change your input features in self.fc1 to 16*5*5=400 and adapt the view call:
class Genv2(nn.Module):
    def __init__(self, *args, **kwargs):
        super(Genv2, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)

        self.fc1 = nn.Linear(in_features=16*5*5, out_features=120)
        self.fc2 = nn.Linear(in_features=120, out_features=84)
        self.out = nn.Linear(in_features=84, out_features=2)

    def forward(self, t):

        print("in forward 1")
        print(t.shape)    # [1, 3, 32, 32]

        # hidden layer
        t = F.relu(self.conv1(t))
        t = F.max_pool2d(t, kernel_size=2, stride=2)
        print("in forward 2")
        print(t.shape)    # [1, 3, 14, 14]
        # hidden layer
        t = F.relu(self.conv2(t))
        t = F.max_pool2d(t, kernel_size=2, stride=2)

        # hidden layer
        t = t.view(t.size(0), -1)
        print(t.shape)
        t = F.relu(self.fc1(t))
        t = F.relu(self.fc2(t))

        # output layer
        t = self.out(t)

        return t