Hey @ptrblck ! A follow up to this, it was a really silly mistake! In the model definition, I had 2 LSTM layers. For the first, I mentioned batch_first=True but for the second, I had forgotten. I have had this code for months and never thought to re write the model definition. Here is the corrected model with batch_first=True for the second layer as well:
class TFModel(nn.Module):
    def __init__(self):
        super().__init__()
        
        self.conv1 = nn.Conv1d(8, 16, kernel_size=8)
        self.conv2 = nn.Conv1d(16, 32, kernel_size=8)
        self.conv3 = nn.Conv1d(32, 64, kernel_size=8)
        
        self.bn1 = nn.BatchNorm1d(64)  # after pooling of 2
        
        self.conv4 = nn.Conv1d(64, 64, kernel_size=8)
        self.conv5 = nn.Conv1d(64, 128, kernel_size=8)
        
        self.bn2 = nn.BatchNorm1d(128)  # after pooling of 2
        
#         self.flat = nn.Flatten()

        self.lstm1 = nn.LSTM(128, 100, batch_first=True)
        self.lstm2 = nn.LSTM(100, 128, batch_first=True)
        
        self.fc1 = nn.Linear(128, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, classes)
        
    def exec_conv_block(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        
        x = F.max_pool1d(x, 2)
#         x = self.bn1(x)
        
        x = F.relu(self.conv4(x))
        x = F.relu(self.conv5(x))
        
        x = F.max_pool1d(x, 2)
#         x = self.bn2(x)
               
        return x
    
    def forward(self, x):
        x = self.exec_conv_block(x)
        
        x, _ = self.lstm1(x.transpose(1, 2))
        x, _ = self.lstm2(x)
        
        x = x[:, -1, :]
        
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        
        return x