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Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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

Files changed (1) hide show
  1. Min1 → Min12 +7 -12
Min1 → Min12 RENAMED
@@ -1 +1 @@
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+ Element-wise min of each of the input tensors (with Numpy-style broadcasting support).
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+ All inputs and outputs must have the same data type.
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+ This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [Broadcasting in ONNX](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).
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- Element-wise min of each of the input tensors. All inputs and outputs must
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- have the same shape and data type.
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-
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- ### Attributes
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-
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- * **consumed_inputs - INTS** :
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-
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- legacy optimization attribute.
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  ### Inputs
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  Between 1 and 2147483647 inputs.
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  - **data_0** (variadic, heterogeneous) - **T**:
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- List of tensors for Min
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+ List of tensors for min.
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  ### Outputs
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  - **min** (heterogeneous) - **T**:
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- Output tensor. Same dimension as inputs.
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+ Output tensor.
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  ### Type Constraints
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- * **T** in ( tensor(double), tensor(float), tensor(float16) ):
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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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- Constrain input and output types to float tensors.? ^^^^^
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+ Constrain input and output types to numeric tensors.? ^^^^^^^