.. _l-onnx-op-reducemin-d1-12:

ReduceMin - 1 vs 12
===================

Next section compares an older to a newer version of the same operator 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/ReduceMin1
    +++ b/ReduceMin12
    @@ -1 +1 @@
      Computes the min of the input tensor's element along the provided axes. The resulting
      tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
    - the resulted tensor have the reduced dimension pruned. Input tensors of rank zero are
    ?                                                       -------------------------------
    + the resulted tensor have the reduced dimension pruned.
    - valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise.
  
      The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
      False instead of True.
  
      ### Attributes
  
      * **axes - INTS** :
  
    -   A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.
    +   A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).
    ?                                                                                                                   ++++++++++++++++++++++++++++++++++++++++++++++++++
  
      * **keepdims - INT** (default is '1'):
  
        Keep the reduced dimension or not, default 1 means keep reduced dimension.
  
      ### Inputs
  
      - **data** (heterogeneous) - **T**:
  
        An input tensor.
  
      ### Outputs
  
      - **reduced** (heterogeneous) - **T**:
  
        Reduced output tensor.
  
      ### Type Constraints
  
    - * **T** in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):
    + * **T** in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8) ):
    ?                                                                                                   ++++++++++++++                       +++++++++++++++
  
    -   Constrain input and output types to high-precision numeric tensors.+   Constrain input and output types to high-precision and 8 bit numeric tensors.?                                                      ++++++++++

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