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Showing content from https://docs.pytorch.org/docs/stable/generated/torch.transpose.html below:

torch.transpose — PyTorch 2.7 documentation

torch.transpose¶

torch.transpose(input, dim0, dim1) → Tensor¶

Returns a tensor that is a transposed version of input. The given dimensions dim0 and dim1 are swapped.

If input is a strided tensor then the resulting out tensor shares its underlying storage with the input tensor, so changing the content of one would change the content of the other.

If input is a sparse tensor then the resulting out tensor does not share the underlying storage with the input tensor.

If input is a sparse tensor with compressed layout (SparseCSR, SparseBSR, SparseCSC or SparseBSC) the arguments dim0 and dim1 must be both batch dimensions, or must both be sparse dimensions. The batch dimensions of a sparse tensor are the dimensions preceding the sparse dimensions.

Note

Transpositions which interchange the sparse dimensions of a SparseCSR or SparseCSC layout tensor will result in the layout changing between the two options. Transposition of the sparse dimensions of a ` SparseBSR` or SparseBSC layout tensor will likewise generate a result with the opposite layout.

Parameters

input (Tensor) – the input tensor.
dim0 (int) – the first dimension to be transposed
dim1 (int) – the second dimension to be transposed

Example:

>>> x = torch.randn(2, 3)
>>> x
tensor([[ 1.0028, -0.9893,  0.5809],
        [-0.1669,  0.7299,  0.4942]])
>>> torch.transpose(x, 0, 1)
tensor([[ 1.0028, -0.1669],
        [-0.9893,  0.7299],
        [ 0.5809,  0.4942]])