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Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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

Files changed (1) hide show
  1. Min1 → Min8 +9 -5
Min1 → Min8 RENAMED
@@ -1 +1 @@
1
- Element-wise min of each of the input tensors (with Numpy-style broadcasting support).
1
+ Element-wise min of each of the input tensors. All inputs and outputs must
2
- All inputs and outputs must have the same data type.
2
+ have the same shape and data type.
3
+
4
+ **Attributes**
5
+
6
+ * **consumed_inputs**:
3
- This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check Broadcasting in ONNX <https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md>_.
7
+ legacy optimization attribute.
4
8
  **Inputs**
5
9
  Between 1 and 2147483647 inputs.
6
10
  * **data_0** (variadic, heterogeneous) - **T**:
7
- List of tensors for min.
11
+ List of tensors for Min
8
12
  **Outputs**
9
13
  * **min** (heterogeneous) - **T**:
10
- Output tensor.
14
+ Output tensor. Same dimension as inputs.
11
15
  **Type Constraints**
12
16
  * **T** in (
13
17
  tensor(double),
14
18
  tensor(float),
15
19
  tensor(float16)
16
20
  ):
17
21
  Constrain input and output types to float tensors.