Div - 6 vs 7

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1. Div6 → Div7 +5 -32

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1	- Performs element-wise binary division (with limited broadcast support).
1	+ Performs element-wise binary division (with Numpy-style broadcasting support).
2	+ 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).
2	- If necessary the right-hand-side argument will be broadcasted to match the
3	- shape of left-hand-side argument. When broadcasting is specified, the second
4	- tensor can either be of element size 1 (including a scalar tensor and any
5	- tensor with rank equal to or smaller than the first tensor), or having its
6	- shape as a contiguous subset of the first tensor's shape. The starting of the
7	- mutually equal shape is specified by the argument "axis", and if it is not set,
8	- suffix matching is assumed. 1-dim expansion doesn't work yet.
9	-
10	- For example, the following tensor shapes are supported (with broadcast=1):
11	-
12	- shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
13	- shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
14	- shape(A) = (2, 3, 4, 5), shape(B) = (5,)
15	- shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
16	- shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
17	- shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
18	-
19	- Attribute broadcast=1 needs to be passed to enable broadcasting.
20	-
21	- ### Attributes
22	-
23	- * **axis - INT** :
24	-
25	- If set, defines the broadcast dimensions. See doc for details.
26	-
27	- * **broadcast - INT** (default is '0'):
28	-
29	- Pass 1 to enable broadcasting
30 3	### Inputs
31 4	- **A** (heterogeneous) - **T**:
32	- First operand, should share the type with the second operand.
5	+ First operand.
33 6	- **B** (heterogeneous) - **T**:
34	- Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.
7	+ Second operand.
35 8	### Outputs
36 9	- **C** (heterogeneous) - **T**:
37	- Result, has same dimensions and type as A
10	+ Result, has same element type as two inputs
38 11	### Type Constraints
39 12	* **T** in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ):
40 13	Constrain input and output types to high-precision numeric tensors.