Sum - 1 vs 8

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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. Sum1 → Sum8 +5 -10

Sum1 → Sum8 RENAMED Viewed

	@@ -1 +1 @@
1	+ Element-wise sum of each of the input tensors (with Numpy-style broadcasting support).
2	+ All inputs and outputs must have the same data type.
3	+ 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).
1	- Element-wise sum of each of the input tensors. All inputs and outputs must
2	- have the same shape and data type.
3	-
4	- ### Attributes
5	-
6	- * **consumed_inputs - INTS** :
7	-
8	- legacy optimization attribute.
9 4	### Inputs
10 5	Between 1 and 2147483647 inputs.
11 6	- **data_0** (variadic, heterogeneous) - **T**:
12	- List of tensors for Sum.
7	+ List of tensors for sum.
13 8	### Outputs
14 9	- **sum** (heterogeneous) - **T**:
15	- Output tensor. Same dimension as inputs.
10	+ Output tensor.
16 11	### Type Constraints
17 12	* **T** in ( tensor(double), tensor(float), tensor(float16) ):
18 13	Constrain input and output types to float tensors.