This video will show you how to Torch Transpose.

Specifically, we will use PyTorch Transpose ( torch.transpose function) to change the order of dimensions in a tensor.

First, we import PyTorch.

```
import torch
```

Then we check the PyTorch version we are using

```
print(torch.__version__)
```

We are using PyTorch version 2.0.0

Next, let's create a PyTorch Tensor full of integers numbers:

```
tensor = torch.tensor([[1, 2, 3],
[4, 5, 6]])
```

To confirm that it's a PyTorch integer tensor, let's use the PyTorch dtype method to check the tensor's data type attribute

```
tensor.dtype
```

We can see that it's "torch.int64" which is a 64-bit integer that is signed.

Because we wrote our tensor manually, we know the dimensions are 2 rows by 3 columns.

However, because transposing tensors in PyTorch is a common operation when working with multi-dimensional data, let's double check the dimensions to make sure.

We use the PyTorch Size operation TORCH.TENSOR.SIZE

```
tensor.size()
```

We see that we get that the size of the tensor is a 2 by 3.

When we do the Torch Transpose we should then be able to check that the new dimensions will be 3 by 2.

Great, now let's now use PyTorch Transpose ( torch.transpose function) to change the order of dimensions of our tensor.

```
transposed_tensor = torch.transpose(tensor, 0, 1)
```

Note that the parameters are
- input (Tensor) – the input tensor.
- dim0 (int) – the first dimension to be transposed
- dim1 (int) – the second dimension to be transposed

If the tensor was 0 dimensional, then it would just return itself.

If the tensor was 1 dimensional, then it would also just return itself.

In our case, because we are transposing a multi-dimensional tensor, we need to put some numbers in.

Now that we've used torch.transpose to do a PyTorch Transpose, let's look at the results:

```
print(transposed_tensor)
```

Then print our original tensor

```
print(tensor)
```

We can see that we have transposed the tensor.

Lastly, just to double check what we are seeing, let's check the dimensions of the transposed tensor.

```
transposed_tensor.size()
```

We see that we get that the size of the tensor is a 3 by 2, where before we had a 2 by 3 tensor.

Perfect!

We used PyTorch Transpose ( torch.transpose function ) to change the order of dimensions in a tensor.