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Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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

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  1. ArgMin1 → ArgMin12 +2 -9
ArgMin1 → ArgMin12 RENAMED
@@ -1 +1 @@
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  Computes the indices of the min elements of the input tensor's element along the
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  provided axis. The resulting tensor has the same rank as the input if keepdims equals 1.
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- If keepdims equal 0, then the resulting tensor has the reduced dimension pruned.
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+ If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.
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- If select_last_index is True (default False), the index of the last occurrence of the min
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- is selected if the min appears more than once in the input. Otherwise the index of the
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- first occurrence is selected.
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  The type of the output tensor is integer.
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  **Attributes**
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  * **axis**:
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- The axis in which to compute the arg indices. Accepted range is [-r,
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+ The axis in which to compute the arg indices.
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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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- * **select_last_index**:
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- Whether to select the last index or the first index if the {name}
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- appears in multiple indices, default is False (first index).
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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) - **tensor(int64)**:
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  Reduced output tensor with integer data type.
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  **Type Constraints**
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  * **T** in (
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  tensor(double),
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  tensor(float),
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  tensor(float16),
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  tensor(int16),
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  tensor(int32),
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  tensor(int64),
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  tensor(int8),
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  tensor(uint16),
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  tensor(uint32),
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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 all numeric tensors.