ReduceMin - 1 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Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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. ReduceMin1 → ReduceMin13 +5 -10
ReduceMin1 → ReduceMin13 RENAMED
@@ -1 +1 @@
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  Computes the min of the input tensor's element along the provided axes. The resulting
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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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+ tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
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- the resulting tensor has the reduced dimension pruned.
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+ the resulted tensor have the reduced dimension pruned.
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  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
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  False instead of True.
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  **Attributes**
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  * **axes**:
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  A list of integers, along which to reduce. The default is to reduce
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- over all the dimensions of the input tensor. Accepted range is [-r,
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+ over all the dimensions of the input tensor.
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- r-1] where r = rank(data).
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  * **keepdims**:
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  Keep the reduced dimension or not, default 1 means keep reduced
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  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 (
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- tensor(bfloat16),
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  tensor(double),
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  tensor(float),
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  tensor(float16),
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  tensor(int32),
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  tensor(int64),
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- tensor(int8),
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  tensor(uint32),
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- tensor(uint64),
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+ tensor(uint64)
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- tensor(uint8)
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  ):
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- Constrain input and output types to high-precision and 8 bit numeric
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+ Constrain input and output types to high-precision numeric tensors.? ^^^^^^^^^
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- tensors.