The following code example illustrates how to integrate cuTensorNet functionalities to perform tensor QR operation. 
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_qr_example.cu>`_).

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Define QR decomposition
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We first define the QR decomposition to perform with the data type, modes partition, and the extents.

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.. note::
   
   `cutensornetTensorQR` operates in reduced mode, expecting the shared extent for the output to be the minimum of 
   the combined row extent and the combined column extent.

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Allocate memory and initialize data
-----------------------------------

Next, we allocate memory for the input and output tensor operands. 
The input operand is initialized to random values.

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Initialize cuTensorNet and create tensor descriptors
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Then we initialize the library handle of cuTensorNet and create `cutensorTensorDescriptor_t` for input and output tensors. 

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Query and allocate required workspace
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Once all tensor descriptors are created, we can query the required workspace size with `cutensornetWorkspaceComputeQRSizes`. 

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Execution
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At this stage, we can perform the QR decomposition by calling `cutensornetTensorQR`.

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

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