def torch_pad_and_concatenate(tensor1, tensor2, padding_index=-100):
    """Concatenates `tensor1` and `tensor2` on first axis, applying padding on the second if necessary."""
    tensor1 = atleast_1d(tensor1)
    tensor2 = atleast_1d(tensor2)

    if len(tensor1.shape) == 1 or tensor1.shape[1] == tensor2.shape[1]:
        return torch.cat((tensor1, tensor2), dim=0)

    # Let's figure out the new shape
    new_shape = (tensor1.shape[0] + tensor2.shape[0], max(tensor1.shape[1], tensor2.shape[1])) + tensor1.shape[2:]

    # Now let's fill the result tensor
    result = tensor1.new_full(new_shape, padding_index)
    result[: tensor1.shape[0], : tensor1.shape[1]] = tensor1
    result[tensor1.shape[0] :, : tensor2.shape[1]] = tensor2
    return result
Concatenates `tensor1` and `tensor2` on first axis, applying padding on the second if necessary.
dpkd/transformers/src/transformers/trainer_pt_utils.py