.. _l-onnx-op-topk-d1-10:

TopK - 1 vs 10
==============

Next section compares an older to a newer version of the same operator 
after both definition are converted into markdown text.
Green means an addition to the newer version, red means a deletion.
Anything else is unchanged.

.. raw:: html


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    --- a/TopK1
    +++ b/TopK10
    @@ -1 +1 @@
      Retrieve the top-K elements along a specified axis. Given an input tensor of
      shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs:
        -Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}]
          which contains the values of the top k elements along the specified axis
        -Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which
         contains the indices of the top k elements (original indices from the input
         tensor).
    + 
      Given two equivalent values, this operator uses the indices along the axis  as
       a tiebreaker. That is, the element with the lower index will appear first.
  
      ### Attributes
  
      * **axis - INT** (default is '-1'):
  
        Dimension on which to do the sort.
  
    - * **k - INT** (required) :
    - 
    -   Number of top elements to retrieve
    - 
      ### Inputs
  
      - **X** (heterogeneous) - **T**:
  
        Tensor of shape [a_0, a_1, ..., a_{n-1}]
    + - **K** (heterogeneous) - **tensor(int64)**:
    + 
    +   A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve
  
      ### Outputs
  
      - **Values** (heterogeneous) - **T**:
  
        Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing top K values from the input tensor
      - **Indices** (heterogeneous) - **I**:
  
        Tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] containing the corresponding input tensor indices for the top K values.
  
      ### Type Constraints
  
      * **T** in ( tensor(double), tensor(float), tensor(float16) ):
  
        Constrain input and output types to float tensors.
      * **I** in ( tensor(int64) ):
  
        Constrain index tensor to int64
    `;

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