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# 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]])

  See also `torch.t()`.

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