ReduceProd - 11 vs 13

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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.

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  1. ReduceProd11 → ReduceProd13 +8 -7
ReduceProd11 → ReduceProd13 RENAMED
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- Computes the product of the input tensor's element along the provided axes. The resulting
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+ Computes the product of the input tensor's elements along the provided axes. The resulting
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- tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
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+ tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
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- the resulted tensor have the reduced dimension pruned.
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+ the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are
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+ valid. Reduction over an empty set of values yields 1.
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- The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
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+ The above behavior is similar to numpy, with the exception that numpy defaults keepdims
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- False instead of True.
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+ to False instead of True.
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  ### Attributes
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  * **axes - INTS** :
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  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).
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  * **keepdims - INT** (default is '1'):
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  Keep the reduced dimension or not, default 1 means keep reduced dimension.
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  ### Inputs
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  - **data** (heterogeneous) - **T**:
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  An input tensor.
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  ### Outputs
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  - **reduced** (heterogeneous) - **T**:
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  Reduced output tensor.
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  ### Type Constraints
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- * **T** in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):
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+ * **T** in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):
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- Constrain input and output types to high-precision numeric tensors.? ---------------
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+ Constrain input and output types to numeric tensors.