Hi @ptrblck thanks for replying.
I have a very simple model
class Network(nn.Module):
    def __init__(self):
        super(Network, self).__init__()
        self.fc1 = nn.Linear(in_features = 41, out_features = 12) 
        self.fc2 = nn.Linear(in_features = 12, out_features = 18)
        self.out = nn.Linear(in_features = 18, out_features = 23) 
    def forward(self, t):
        t = F.relu(self.fc1(t))
        t = F.relu(self.fc2(t))
        t = self.out(t)
        return t

Using the same data sample:
Output tensor from the training script -
tensor([[-5.8796,  0.1122, -2.8794,  0.9305, -3.4934, -7.0429, -0.1777, -2.3283,
         -1.0330, -3.4359, -3.4113,  1.5217, -1.6634, -1.9250, -5.6562, -3.7723,
         -1.8225, -4.3986, -4.6337, -2.6478, -5.8153, -2.6518, -0.4750]])

Output tensor from the exported model -
tensor([[-3.4793, -2.1187, -4.3994, -1.4864, -4.5343, -3.9016, -3.1163, -2.0864,
         -1.4641, -3.9484, -4.8409,  1.2172, -3.8068, -4.4060, -2.3133, -3.9828,
         -4.2283, -5.3425,  0.6747, -4.3991, -4.5983, -3.2529, -2.3505]])


In this instance, they both correctly classified the data as class [11].
However, when I reran the script, this time with a different data sample, the output from the exported model was exactly the same as above:
tensor([[-3.4793, -2.1187, -4.3994, -1.4864, -4.5343, -3.9016, -3.1163, -2.0864,
         -1.4641, -3.9484, -4.8409,  1.2172, -3.8068, -4.4060, -2.3133, -3.9828,
         -4.2283, -5.3425,  0.6747, -4.3991, -4.5983, -3.2529, -2.3505]])