In short, the code looks like this
result = []
for i in range(len(data)):
    result.append(data[i])
result = torch.FloatTensor(np.concatenate(result), axis = 0)

So, the two tensor (100,3) (200,3) will combine into one tensor(300,3). I use another variable to count the number of rows of each tensor. As you can see, I lose the batch dimension by doing this. It’s ok for single GPU, but batch dimension is necessary for multi-GPU case.