Performing tensor SVD using cuTensorNet adopts a very similar workflow as :ref:`QR example<QRexample>`. 
Here, we highlight the notable differences between the two APIs. The full code can be found in 
the `NVIDIA/cuQuantum <https://github.com/NVIDIA/cuQuantum>`_ repository (`here <https://github.com/NVIDIA/cuQuantum/blob/main/samples/cutensornet/approxTN/tensor_svd_example.cu>`_).

------------------------
Define SVD decomposition
------------------------

As with QR decomposition, we first define the SVD decomposition to perform with the data type, modes partition, and the extents.

.. literalinclude:: ../../../../tensor_network/samples/approxTN/tensor_svd_example.cu
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.. note::
   
   To perform fixed extent truncation, we directly set ``maxExtent`` to half of the full extent corresponding to exact SVD.

-------------------------------
Setup SVD truncation parameters
-------------------------------

Once the SVD decomposition is defined, we can follow the same workflow as :ref:`QR example<QRexample>` for data allocation and tensor descriptor initialization. 
Before querying workspace, we can choose different SVD options in `cutensornetTensorSVDConfig_t`. 
Meanwhile, we can create `cutensornetTensorSVDInfo_t` to keep track of runtime truncation information.

.. literalinclude:: ../../../../tensor_network/samples/approxTN/tensor_svd_example.cu
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---------
Execution
---------

Next, we can query and allocate the workspace with `cutensornetWorkspaceComputeSVDSizes`, which is very similar to its QR counterpart. 
At this stage, we can perform the SVD decomposition by calling `cutensornetTensorSVD`.

.. literalinclude:: ../../../../tensor_network/samples/approxTN/tensor_svd_example.cu
   :language: c++
   :linenos:
   :lineno-match:
   :start-after: Sphinx: #7
   :end-before: Sphinx: #8

.. note::
   
   Since we turned on weighted truncation options in this example, we need to restore the tensor descriptors for U and V if we 
   wish to perform the same computation multiple times.

After the computation, we still need to free up all resources.