Not sure what you are trying to do, but your output tensor has size 2 while the RNN model has output of size 6. You may want to add a linear fully connected layer to match the desired output tensor size.
Modifying your code slightly:
class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()
        self.rnn = nn.LSTM(
            input_size=6,
            hidden_size=6,
            num_layers=2,
            batch_first=True,
        )
        
        self.fc = nn.Linear(6,2)

    def forward(self, x):
        out, (h_n, h_c) = self.rnn(x, None)
        #print(out)
        return self.fc(out[:, -1, :])    # Return output at last time-step

Here is an example notebook of using the RNN model with the 1 sample example you provided: https://colab.research.google.com/drive/1N-TLRFvvbmSqoyFrhj9_ZcvKEdeVaS3a 20