I was going through this example of a LSTM language model on github [(link)][1].
What it does in general is pretty clear to me. But I&#39;m still struggling to understand what calling `contiguous()` does, which occurs several times in the code.

For example in line 74/75 of the code input and target sequences of the LSTM are created.
Data (stored in `ids`) is 2-dimensional where first dimension is the batch size.

&lt;!-- language-all: lang-python --&gt;

    for i in range(0, ids.size(1) - seq_length, seq_length):
        # Get batch inputs and targets
        inputs = Variable(ids[:, i:i+seq_length])
        targets = Variable(ids[:, (i+1):(i+1)+seq_length].contiguous())

So as a simple example, when using batch size 1 and `seq_length` 10 `inputs` and `targets` looks like this:

    inputs Variable containing:
    0     1     2     3     4     5     6     7     8     9
    [torch.LongTensor of size 1x10]

    targets Variable containing:
    1     2     3     4     5     6     7     8     9    10
    [torch.LongTensor of size 1x10]

So in general my question is, what does `contiguous()` do and why do I need it?

Further I don&#39;t understand why the method is called for the target sequence and but not the input sequence as both variables are comprised of the same data.

How could `targets` be non-contiguous and `inputs` still be contiguous?

---

**EDIT:**

*I tried to leave out calling `contiguous()`, but this leads to an  error message when computing the loss.*

    RuntimeError: invalid argument 1: input is not contiguous at .../src/torch/lib/TH/generic/THTensor.c:231

*So obviously calling `contiguous()` in this example is necessary.* 

  [1]: https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/02-intermediate/language_model/main.py

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PyTorch - What does contiguous() do?