ArgMin - 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Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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. ArgMin11 → ArgMin13 +9 -3
ArgMin11 → ArgMin13 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 equals 0, then the resulting tensor has 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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- The input tensor must not be empty.
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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 - INT** (default is '0'):
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  The axis in which to compute the arg indices. 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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+
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+ * **select_last_index - INT** (default is '0'):
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+ Whether to select the last index or the first index if the {name} 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 ( tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
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+ * **T** in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
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  Constrain input and output types to all numeric tensors.