ReduceProd - 1 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Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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.

Files changed (1) hide show
  1. ReduceProd1 → ReduceProd13 +3 -5
ReduceProd1 → ReduceProd13 RENAMED
@@ -1 +1 @@
1
1
  Computes the product of the input tensor's element along the provided axes. The resulting
2
- tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then
2
+ tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then
3
- the resulting tensor has the reduced dimension pruned.
3
+ the resulted tensor have the reduced dimension pruned.
4
4
  The above behavior is similar to numpy, with the exception that numpy defaults keepdims to
5
5
  False instead of True.
6
6
  **Attributes**
7
7
  * **axes**:
8
8
  A list of integers, along which to reduce. The default is to reduce
9
- over all the dimensions of the input tensor. Accepted range is [-r,
9
+ over all the dimensions of the input tensor.
10
- r-1] where r = rank(data).
11
10
  * **keepdims**:
12
11
  Keep the reduced dimension or not, default 1 means keep reduced
13
12
  dimension.
14
13
  **Inputs**
15
14
  * **data** (heterogeneous) - **T**:
16
15
  An input tensor.
17
16
  **Outputs**
18
17
  * **reduced** (heterogeneous) - **T**:
19
18
  Reduced output tensor.
20
19
  **Type Constraints**
21
20
  * **T** in (
22
- tensor(bfloat16),
23
21
  tensor(double),
24
22
  tensor(float),
25
23
  tensor(float16),
26
24
  tensor(int32),
27
25
  tensor(int64),
28
26
  tensor(uint32),
29
27
  tensor(uint64)
30
28
  ):
31
29
  Constrain input and output types to high-precision numeric tensors.