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bulk change type T to type *T* in spec (#6486)

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* bulk change `type T` to `type *T*` in spec

* update all `T` which referee to type to use *T* pattern

* get back with `T` where T is dimension

* fix *T*1 -> *T1*

* Make italic types where was no formating
This commit is contained in:
Patryk Elszkowski 2021-07-02 12:51:00 +02:00 committed by GitHub
parent ccf786438b
commit de53c40578
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GPG Key ID: 4AEE18F83AFDEB23
61 changed files with 253 additions and 257 deletions
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6
docs/ops/activation/Clamp_1.md
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@ -38,16 +38,16 @@ clamp( x_{i} )=\min\big( \max\left( x_{i}, min\_value \right), max\_value \big)
**Inputs**:
* **1**: A tensor of type `T` and arbitrary shape. **Required**.
* **1**: A tensor of type *T* and arbitrary shape. **Required**.
**Outputs**:
* **1**: A tensor of type `T` with same shape as input tensor.
* **1**: A tensor of type *T* with same shape as input tensor.
**Types**
* *T*: any numeric type.
* **Note**: In case of integral numeric type, ceil is used to convert *min* from `float` to `T` and floor is used to convert *max* from `float` to `T`.
* **Note**: In case of integral numeric type, ceil is used to convert *min* from `float` to *T* and floor is used to convert *max* from `float` to *T*.
**Example**

4
docs/ops/activation/Elu_1.md
View File

@ -34,11 +34,11 @@ where α corresponds to *alpha* attribute.
**Inputs**:
* **1**: A tensor of type `T` and arbitrary shape. **Required**.
* **1**: A tensor of type *T* and arbitrary shape. **Required**.
**Outputs**:
* **1**: The result of element-wise *Elu* function applied to the input tensor. A tensor of type `T` and the same shape as input tensor.
* **1**: The result of element-wise *Elu* function applied to the input tensor. A tensor of type *T* and the same shape as input tensor.
**Types**

4
docs/ops/activation/Exp_1.md
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@ -18,11 +18,11 @@ exp(x) = e^{x}
**Inputs**
* **1**: A tensor of type `T` and arbitrary shape. **Required**.
* **1**: A tensor of type *T* and arbitrary shape. **Required**.
**Outputs**
* **1**: The result of element-wise *Exp* function applied to the input tensor. A tensor of type `T` and the same shape as input tensor.
* **1**: The result of element-wise *Exp* function applied to the input tensor. A tensor of type *T* and the same shape as input tensor.
**Types**

4
docs/ops/activation/GELU_2.md
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@ -27,11 +27,11 @@ Additionally, *Gelu* function may be approximated as follows:
**Inputs**:
* **1**: A tensor of type `T` and arbitrary shape. **Required**.
* **1**: A tensor of type *T* and arbitrary shape. **Required**.
**Outputs**:
* **1**: The result of element-wise *Gelu* function applied to the input tensor. A tensor of type `T` and the same shape as input tensor.
* **1**: The result of element-wise *Gelu* function applied to the input tensor. A tensor of type *T* and the same shape as input tensor.
**Types**

8
docs/ops/activation/HardSigmoid_1.md
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@ -18,15 +18,15 @@ For each element from the input tensor calculates corresponding
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
* **2**: `alpha` 0D tensor (scalar) of type T. **Required.**
* **2**: `alpha` 0D tensor (scalar) of type *T*. **Required.**
* **3**: `beta` 0D tensor (scalar) of type T. **Required.**
* **3**: `beta` 0D tensor (scalar) of type *T*. **Required.**
**Outputs**
* **1**: The result of the hard sigmoid operation. A tensor of type T.
* **1**: The result of the hard sigmoid operation. A tensor of type *T*.
**Types**

4
docs/ops/activation/LogSoftmax_5.md
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@ -24,11 +24,11 @@ LogSoftmax(x, axis) = t - Log(ReduceSum(Exp(t), axis))
**Inputs**:
* **1**: Input tensor *x* of type T with enough number of dimension to be compatible with *axis* attribute. Required.
* **1**: Input tensor *x* of type *T* with enough number of dimension to be compatible with *axis* attribute. Required.
**Outputs**:
* **1**: The resulting tensor of the same shape and of type T.
* **1**: The resulting tensor of the same shape and of type *T*.
**Types**

4
docs/ops/activation/Mish_4.md
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@ -20,11 +20,11 @@ Mish(x) = x\cdot\tanh\big(SoftPlus(x)\big) = x\cdot\tanh\big(\ln(1+e^{x})\big)
**Inputs**:
* **1**: A tensor of type `T` and arbitrary shape. **Required**.
* **1**: A tensor of type *T* and arbitrary shape. **Required**.
**Outputs**:
* **1**: The result of element-wise *Mish* function applied to the input tensor. A tensor of type `T` and the same shape as input tensor.
* **1**: The result of element-wise *Mish* function applied to the input tensor. A tensor of type *T* and the same shape as input tensor.
**Types**

8
docs/ops/activation/Selu_1.md
View File

@ -31,15 +31,15 @@ Selu(x) = \lambda\cdot\big(\max(0, x) + \min(0, \alpha(e^{x}-1))\big)
**Inputs**
* **1**: `data`. A tensor of type `T` and arbitrary shape. **Required.**
* **1**: `data`. A tensor of type *T* and arbitrary shape. **Required.**
* **2**: `alpha`. 1D tensor with one element of type `T`. **Required.**
* **2**: `alpha`. 1D tensor with one element of type *T*. **Required.**
* **3**: `lambda`. 1D tensor with one element of type `T`. **Required.**
* **3**: `lambda`. 1D tensor with one element of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise *Selu* function applied to `data` input tensor. A tensor of type `T` and the same shape as `data` input tensor.
* **1**: The result of element-wise *Selu* function applied to `data` input tensor. A tensor of type *T* and the same shape as `data` input tensor.
**Types**

4
docs/ops/activation/SoftPlus_4.md
View File

@ -35,11 +35,11 @@ For example, if *T* is `fp32`, `threshold` should be `20` or if *T* is `fp16`, `
**Inputs**:
* **1**: A tensor of type `T` and arbitrary shape. **Required**.
* **1**: A tensor of type *T* and arbitrary shape. **Required**.
**Outputs**:
* **1**: The result of element-wise *SoftPlus* function applied to the input tensor. A tensor of type `T` and the same shape as input tensor.
* **1**: The result of element-wise *SoftPlus* function applied to the input tensor. A tensor of type *T* and the same shape as input tensor.
**Types**

6
docs/ops/activation/Swish_4.md
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@ -22,13 +22,13 @@ where β corresponds to `beta` scalar input.
**Inputs**:
* **1**: `data`. A tensor of type `T` and arbitrary shape. **Required**.
* **1**: `data`. A tensor of type *T* and arbitrary shape. **Required**.
* **2**: `beta`. A non-negative scalar value of type `T`. Multiplication parameter for the sigmoid. Default value 1.0 is used. **Optional**.
* **2**: `beta`. A non-negative scalar value of type *T*. Multiplication parameter for the sigmoid. Default value 1.0 is used. **Optional**.
**Outputs**:
* **1**: The result of element-wise *Swish* function applied to the input tensor `data`. A tensor of type `T` and the same shape as `data` input tensor.
* **1**: The result of element-wise *Swish* function applied to the input tensor `data`. A tensor of type *T* and the same shape as `data` input tensor.
**Types**

5
docs/ops/arithmetic/Abs_1.md
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@ -12,11 +12,11 @@
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise abs operation. A tensor of type T.
* **1**: The result of element-wise abs operation. A tensor of type *T*.
**Types**
@ -48,4 +48,3 @@ a_{i} = abs(a_{i})
</output>
</layer>
```

4
docs/ops/arithmetic/Acos_1.md
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@ -12,11 +12,11 @@
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise acos operation. A tensor of type T.
* **1**: The result of element-wise acos operation. A tensor of type *T*.
**Types**

4
docs/ops/arithmetic/Acosh_3.md
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@ -12,11 +12,11 @@
**Inputs**
* **1**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise acosh operation. A tensor of type T.
* **1**: The result of element-wise acosh operation. A tensor of type *T*.
**Types**

6
docs/ops/arithmetic/Add_1.md
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@ -29,12 +29,12 @@ o_{i} = a_{i} + b_{i}
**Inputs**
* **1**: A tensor of type T and arbitrary shape and rank. **Required.**
* **2**: A tensor of type T and arbitrary shape and rank. **Required.**
* **1**: A tensor of type *T* and arbitrary shape and rank. **Required.**
* **2**: A tensor of type *T* and arbitrary shape and rank. **Required.**
**Outputs**
* **1**: The result of element-wise addition operation. A tensor of type T with shape equal to broadcasted shape of the two inputs.
* **1**: The result of element-wise addition operation. A tensor of type *T* with shape equal to broadcasted shape of the two inputs.
**Types**

4
docs/ops/arithmetic/Asin_1.md
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@ -12,11 +12,11 @@
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise asin operation. A tensor of type T.
* **1**: The result of element-wise asin operation. A tensor of type *T*.
**Types**

4
docs/ops/arithmetic/Asinh_3.md
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@ -12,11 +12,11 @@
**Inputs**
* **1**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise asinh operation. A tensor of type T.
* **1**: The result of element-wise asinh operation. A tensor of type *T*.
**Types**

4
docs/ops/arithmetic/Atan_1.md
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@ -12,11 +12,11 @@
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise atan operation. A tensor of type T.
* **1**: The result of element-wise atan operation. A tensor of type *T*.
**Types**

4
docs/ops/arithmetic/Atanh_3.md
View File

@ -12,11 +12,11 @@
**Inputs**
* **1**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise atanh operation. A tensor of type T.
* **1**: The result of element-wise atanh operation. A tensor of type *T*.
**Types**

4
docs/ops/arithmetic/Cosh_1.md
View File

@ -12,11 +12,11 @@
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise cosh operation. A tensor of type T.
* **1**: The result of element-wise cosh operation. A tensor of type *T*.
**Types**

6
docs/ops/arithmetic/CumSum_3.md
View File

@ -32,13 +32,13 @@
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
* **2**: Scalar axis of type T_AXIS. Negative value means counting dimensions from the back. Default value is 0. **Optional.**
* **2**: Scalar axis of type *T_AXIS*. Negative value means counting dimensions from the back. Default value is 0. **Optional.**
**Outputs**
* **1**: Output tensor with cumulative sums of the input's elements. A tensor of type T of the same shape as 1st input.
* **1**: Output tensor with cumulative sums of the input's elements. A tensor of type *T* of the same shape as 1st input.
**Types**

6
docs/ops/arithmetic/Divide_1.md
View File

@ -41,12 +41,12 @@ The result of division by zero is undefined.
**Inputs**
* **1**: A tensor of type T and arbitrary shape and rank. **Required.**
* **2**: A tensor of type T and arbitrary shape and rank. **Required.**
* **1**: A tensor of type *T* and arbitrary shape and rank. **Required.**
* **2**: A tensor of type *T* and arbitrary shape and rank. **Required.**
**Outputs**
* **1**: The result of element-wise division operation. A tensor of type T with shape equal to broadcasted shape of the two inputs.
* **1**: The result of element-wise division operation. A tensor of type *T* with shape equal to broadcasted shape of the two inputs.
**Types**

4
docs/ops/arithmetic/Erf_1.md
View File

@ -19,11 +19,11 @@ erf(x) = \pi^{-1} \int_{-x}^{x} e^{-t^2} dt
**Inputs**
* **1**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise operation. A tensor of type T.
* **1**: The result of element-wise operation. A tensor of type *T*.
**Types**

6
docs/ops/arithmetic/FloorMod_1.md
View File

@ -29,12 +29,12 @@ o_{i} = a_{i} % b_{i}
**Inputs**
* **1**: A tensor of type T and arbitrary shape. Required.
* **2**: A tensor of type T and arbitrary shape. Required.
* **1**: A tensor of type *T* and arbitrary shape. Required.
* **2**: A tensor of type *T* and arbitrary shape. Required.
**Outputs**
* **1**: The result of element-wise floor modulo operation. A tensor of type T with shape equal to broadcasted shape of two inputs.
* **1**: The result of element-wise floor modulo operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs.
**Types**

4
docs/ops/arithmetic/Log_1.md
View File

@ -18,11 +18,11 @@ a_{i} = log(a_{i})
**Inputs**
* **1**: An tensor of type T and arbitrary shape. **Required.**
* **1**: An tensor of type *T* and arbitrary shape. **Required.**
**Outputs**
* **1**: The result of element-wise log operation. A tensor of type T and the same shape as input.
* **1**: The result of element-wise log operation. A tensor of type *T* and the same shape as input.
**Types**

6
docs/ops/arithmetic/Maximum_1.md
View File

@ -29,12 +29,12 @@ o_{i} = max(a_{i}, b_{i})
**Inputs**
* **1**: A tensor of type T and arbitrary shape. Required.
* **2**: A tensor of type T and arbitrary shape. Required.
* **1**: A tensor of type *T* and arbitrary shape. Required.
* **2**: A tensor of type *T* and arbitrary shape. Required.
**Outputs**
* **1**: The result of element-wise maximum operation. A tensor of type T with shape equal to broadcasted shape of two inputs.
* **1**: The result of element-wise maximum operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs.
**Types**

6
docs/ops/arithmetic/Minimum_1.md
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@ -27,12 +27,12 @@ o_{i} = min(a_{i}, b_{i})
**Inputs**
* **1**: A tensor of type T and arbitrary shape. Required.
* **2**: A tensor of type T and arbitrary shape. Required.
* **1**: A tensor of type *T* and arbitrary shape. Required.
* **2**: A tensor of type *T* and arbitrary shape. Required.
**Outputs**
* **1**: The result of element-wise minimum operation. A tensor of type T with shape equal to broadcasted shape of two inputs.
* **1**: The result of element-wise minimum operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs.
**Types**

6
docs/ops/arithmetic/Mod_1.md
View File

@ -30,12 +30,12 @@ o_{i} = a_{i} % b_{i}
**Inputs**
* **1**: A tensor of type T and arbitrary shape. Required.
* **2**: A tensor of type T and arbitrary shape. Required.
* **1**: A tensor of type *T* and arbitrary shape. Required.
* **2**: A tensor of type *T* and arbitrary shape. Required.
**Outputs**
* **1**: The result of element-wise modulo operation. A tensor of type T with shape equal to broadcasted shape of two inputs.
* **1**: The result of element-wise modulo operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs.
**Types**

6
docs/ops/arithmetic/Multiply_1.md
View File

@ -29,12 +29,12 @@ o_{i} = a_{i} * b_{i}
**Inputs**
* **1**: A tensor of type T and arbitrary shape and rank. **Required.**
* **2**: A tensor of type T and arbitrary shape and rank. **Required.**
* **1**: A tensor of type *T* and arbitrary shape and rank. **Required.**
* **2**: A tensor of type *T* and arbitrary shape and rank. **Required.**
**Outputs**
* **1**: The result of element-wise multiplication operation. A tensor of type T with shape equal to broadcasted shape of the two inputs.
* **1**: The result of element-wise multiplication operation. A tensor of type *T* with shape equal to broadcasted shape of the two inputs.
**Types**

6
docs/ops/arithmetic/Power_1.md
View File

@ -27,12 +27,12 @@ o_{i} = {a_{i} ^ b_{i}}
**Inputs**
* **1**: A tensor of type T and arbitrary shape. Required.
* **2**: A tensor of type T and arbitrary shape. Required.
* **1**: A tensor of type *T* and arbitrary shape. Required.
* **2**: A tensor of type *T* and arbitrary shape. Required.
**Outputs**
* **1**: The result of element-wise power operation. A tensor of type T with shape equal to broadcasted shape of two inputs.
* **1**: The result of element-wise power operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs.
**Types**

4
docs/ops/arithmetic/Sign_1.md
View File

@ -12,11 +12,11 @@
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise sign operation. A tensor of type T with mapped elements of the input tensor to -1 (if it is negative), 0 (if it is zero), or 1 (if it is positive).
* **1**: The result of element-wise sign operation. A tensor of type *T* with mapped elements of the input tensor to -1 (if it is negative), 0 (if it is zero), or 1 (if it is positive).
**Types**

4
docs/ops/arithmetic/Sin_1.md
View File

@ -19,11 +19,11 @@ a - value representing angle in radians.
**Inputs**
* **1**: An tensor of type T and arbitrary rank. **Required.**
* **1**: An tensor of type *T* and arbitrary rank. **Required.**
**Outputs**
* **1**: The result of element-wise sin operation. A tensor of type T.
* **1**: The result of element-wise sin operation. A tensor of type *T*.
**Types**

2
docs/ops/arithmetic/Sinh_1.md
View File

@ -12,7 +12,7 @@
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
**Outputs**

6
docs/ops/arithmetic/SquaredDifference_1.md
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@ -27,12 +27,12 @@ o_{i} = (a_{i} - b_{i})^2
**Inputs**
* **1**: A tensor of type T and arbitrary shape. Required.
* **2**: A tensor of type T and arbitrary shape. Required.
* **1**: A tensor of type *T* and arbitrary shape. Required.
* **2**: A tensor of type *T* and arbitrary shape. Required.
**Outputs**
* **1**: The result of element-wise subtract and square the result operation. A tensor of type T with shape equal to broadcasted shape of two inputs.
* **1**: The result of element-wise subtract and square the result operation. A tensor of type *T* with shape equal to broadcasted shape of two inputs.
**Types**

6
docs/ops/arithmetic/Subtract_1.md
View File

@ -29,12 +29,12 @@ o_{i} = a_{i} - b_{i}
**Inputs**
* **1**: A tensor of type T and arbitrary shape and rank. **Required.**
* **2**: A tensor of type T and arbitrary shape and rank. **Required.**
* **1**: A tensor of type *T* and arbitrary shape and rank. **Required.**
* **2**: A tensor of type *T* and arbitrary shape and rank. **Required.**
**Outputs**
* **1**: The result of element-wise subtraction operation. A tensor of type T with shape equal to broadcasted shape of the two inputs.
* **1**: The result of element-wise subtraction operation. A tensor of type *T* with shape equal to broadcasted shape of the two inputs.
**Types**

5
docs/ops/arithmetic/Tan_1.md
View File

@ -12,11 +12,11 @@
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise tan operation. A tensor of type T.
* **1**: The result of element-wise tan operation. A tensor of type *T*.
**Types**
@ -48,4 +48,3 @@ a_{i} = tan(a_{i})
</output>
</layer>
```

4
docs/ops/comparison/Equal_1.md
View File

@ -20,8 +20,8 @@
**Inputs**
* **1**: A tensor of type T. **Required.**
* **2**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
* **2**: A tensor of type *T*. **Required.**
**Outputs**

4
docs/ops/comparison/GreaterEqual_1.md
View File

@ -20,8 +20,8 @@
**Inputs**
* **1**: A tensor of type T. **Required.**
* **2**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
* **2**: A tensor of type *T*. **Required.**
**Outputs**

4
docs/ops/comparison/Greater_1.md
View File

@ -20,8 +20,8 @@
**Inputs**
* **1**: A tensor of type T. **Required.**
* **2**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
* **2**: A tensor of type *T*. **Required.**
**Outputs**

4
docs/ops/comparison/LessEqual_1.md
View File

@ -20,8 +20,8 @@
**Inputs**
* **1**: A tensor of type T. **Required.**
* **2**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
* **2**: A tensor of type *T*. **Required.**
**Outputs**

4
docs/ops/comparison/Less_1.md
View File

@ -20,8 +20,8 @@
**Inputs**
* **1**: A tensor of type T. **Required.**
* **2**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
* **2**: A tensor of type *T*. **Required.**
**Outputs**

4
docs/ops/comparison/NotEqual_1.md
View File

@ -20,8 +20,8 @@
**Inputs**
* **1**: A tensor of type T. **Required.**
* **2**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
* **2**: A tensor of type *T*. **Required.**
**Outputs**

4
docs/ops/detection/RegionYolo_1.md
View File

@ -78,11 +78,11 @@
**Inputs**:
* **1**: `data` - 4D tensor of type `T` and shape `[N, C, H, W]`. **Required.**
* **1**: `data` - 4D tensor of type *T* and shape `[N, C, H, W]`. **Required.**
**Outputs**:
* **1**: tensor of type `T` and rank 4 or less that codes detected regions. Refer to the [YOLO9000: Better, Faster, Stronger](https://arxiv.org/pdf/1612.08242.pdf) paper to decode the output as boxes. `anchors` should be used to decode real box coordinates. If `do_softmax` is set to `0`, then the output shape is `[N, (classes + coords + 1) * len(mask), H, W]`. If `do_softmax` is set to `1`, then output shape is partially flattened and defined in the following way:
* **1**: tensor of type *T* and rank 4 or less that codes detected regions. Refer to the [YOLO9000: Better, Faster, Stronger](https://arxiv.org/pdf/1612.08242.pdf) paper to decode the output as boxes. `anchors` should be used to decode real box coordinates. If `do_softmax` is set to `0`, then the output shape is `[N, (classes + coords + 1) * len(mask), H, W]`. If `do_softmax` is set to `1`, then output shape is partially flattened and defined in the following way:
`flat_dim = data.shape[axis] * data.shape[axis+1] * ... * data.shape[end_axis]`
`output.shape = [data.shape[0], ..., data.shape[axis-1], flat_dim, data.shape[end_axis + 1], ...]`

9
docs/ops/generation/Range_1.md
View File

@ -12,13 +12,13 @@ No attributes available.
**Inputs**:
* **1**: "start" - A scalar of type T. **Required.**
* **2**: "stop" - A scalar of type T. **Required.**
* **3**: "step" - A scalar of type T. **Required.**
* **1**: "start" - A scalar of type *T*. **Required.**
* **2**: "stop" - A scalar of type *T*. **Required.**
* **3**: "step" - A scalar of type *T*. **Required.**
**Outputs**:
* **1**: A tensor of type T.
* **1**: A tensor of type *T*.
**Types**
@ -87,4 +87,3 @@ val[i]=start+i*step
</output>
</layer>
```

7
docs/ops/generation/Range_4.md
View File

@ -18,9 +18,9 @@
**Inputs**:
* **1**: "start" - A scalar of type T1. **Required.**
* **2**: "stop" - A scalar of type T2. **Required.**
* **3**: "step" - A scalar of type T3. If `step` is equal to zero after casting to `output_type`, behavior is undefined. **Required.**
* **1**: "start" - A scalar of type *T1*. **Required.**
* **2**: "stop" - A scalar of type *T2*. **Required.**
* **3**: "step" - A scalar of type *T3*. If `step` is equal to zero after casting to `output_type`, behavior is undefined. **Required.**
**Outputs**:
@ -124,4 +124,3 @@ This is aligned with PyTorch's operation `torch.arange`, to align with tensorflo
</output>
</layer>
```

8
docs/ops/image/Interpolate_4.md
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@ -89,13 +89,13 @@
**Inputs**
* **1**: `data` - tensor of type `T` with data for interpolation. **Required.**
* **1**: `data` - tensor of type *T* with data for interpolation. **Required.**
* **2**: `sizes` - 1D tensor of type `T_SIZE` describing output shape for spatial axes. Number of elements matches the number of indices in `axes` input, the order matches as well. **Required.**
* **2**: `sizes` - 1D tensor of type *T_SIZE* describing output shape for spatial axes. Number of elements matches the number of indices in `axes` input, the order matches as well. **Required.**
* **3**: `scales` - 1D tensor of type `T_SCALES` describing scales for spatial axes. Number and order of elements match the number and order of indices in `axes` input. **Required.**
* **3**: `scales` - 1D tensor of type *T_SCALES* describing scales for spatial axes. Number and order of elements match the number and order of indices in `axes` input. **Required.**
* **4**: `axes` - 1D tensor of type `T_AXES` specifying dimension indices where interpolation is applied, and `axes` is any unordered list of indices of different dimensions of input tensor, e.g. `[0, 4]`, `[4, 0]`, `[4, 2, 1]`, `[1, 2, 3]`. These indices should be non-negative integers from `0` to `rank(data) - 1` inclusively. Other dimensions do not change. The order of elements in `axes` attribute matters, and mapped directly to elements in the 2nd input `sizes`. **Optional** with default value `[0,...,rank(data) - 1]`.
* **4**: `axes` - 1D tensor of type *T_AXES* specifying dimension indices where interpolation is applied, and `axes` is any unordered list of indices of different dimensions of input tensor, e.g. `[0, 4]`, `[4, 0]`, `[4, 2, 1]`, `[1, 2, 3]`. These indices should be non-negative integers from `0` to `rank(data) - 1` inclusively. Other dimensions do not change. The order of elements in `axes` attribute matters, and mapped directly to elements in the 2nd input `sizes`. **Optional** with default value `[0,...,rank(data) - 1]`.
**Outputs**

2
docs/ops/infrastructure/Result_1.md
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@ -12,7 +12,7 @@
**Inputs**
* **1**: A tensor of type T. **Required.**
* **1**: A tensor of type *T*. **Required.**
**Types**

4
docs/ops/logical/LogicalAnd_1.md
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@ -20,8 +20,8 @@
**Inputs**
* **1**: A tensor of type T. **Required**.
* **2**: A tensor of type T. **Required**.
* **1**: A tensor of type *T*. **Required**.
* **2**: A tensor of type *T*. **Required**.
**Outputs**

4
docs/ops/logical/LogicalNot_1.md
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@ -12,11 +12,11 @@
**Inputs**
* **1**: An tensor of type T. **Required.**
* **1**: An tensor of type *T*. **Required.**
**Outputs**
* **1**: The result of element-wise logical negation operation. A tensor of type T.
* **1**: The result of element-wise logical negation operation. A tensor of type *T*.
**Types**

4
docs/ops/logical/LogicalOr_1.md
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@ -20,8 +20,8 @@
**Inputs**
* **1**: A tensor of type T. **Required**.
* **2**: A tensor of type T. **Required**.
* **1**: A tensor of type *T*. **Required**.
* **2**: A tensor of type *T*. **Required**.
**Outputs**

6
docs/ops/logical/LogicalXor_1.md
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@ -20,12 +20,12 @@
**Inputs**
* **1**: A tensor of type T. **Required**.
* **2**: A tensor of type T. **Required**.
* **1**: A tensor of type *T*. **Required**.
* **2**: A tensor of type *T*. **Required**.
**Outputs**
* **1**: The result of element-wise logical XOR operation. A tensor of type T.
* **1**: The result of element-wise logical XOR operation. A tensor of type *T*.
**Types**

4
docs/ops/movement/DepthToSpace_1.md
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@ -48,11 +48,11 @@ If `mode = depth_first`:
**Inputs**
* **1**: `data` - input tensor of type T with rank >= 3. **Required**.
* **1**: `data` - input tensor of type *T* with rank >= 3. **Required**.
**Outputs**
* **1**: permuted tensor of type T and shape `[N, C / block_size ^ K, D1 * block_size, D2 * block_size, ..., DK * block_size]`.
* **1**: permuted tensor of type *T* and shape `[N, C / block_size ^ K, D1 * block_size, D2 * block_size, ..., DK * block_size]`.
**Types**

10
docs/ops/movement/GatherTree_1.md
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@ -43,18 +43,18 @@ Element data types for all input tensors should match each other.
**Inputs**
* **1**: `step_ids` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type `T` with indices from per each step. Required.
* **1**: `step_ids` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type *T* with indices from per each step. Required.
* **2**: `parent_idx` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type `T` with parent beam indices. Required.
* **2**: `parent_idx` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type *T* with parent beam indices. Required.
* **3**: `max_seq_len` -- a tensor of shape `[BATCH_SIZE]` of type `T` with maximum lengths for each sequence in the batch. Required.
* **3**: `max_seq_len` -- a tensor of shape `[BATCH_SIZE]` of type *T* with maximum lengths for each sequence in the batch. Required.
* **4**: `end_token` -- a scalar tensor of type `T` with value of the end marker in a sequence. Required.
* **4**: `end_token` -- a scalar tensor of type *T* with value of the end marker in a sequence. Required.
**Outputs**
* **1**: `final_idx` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type `T`.
* **1**: `final_idx` -- a tensor of shape `[MAX_TIME, BATCH_SIZE, BEAM_WIDTH]` of type *T*.
**Types**

6
docs/ops/movement/Split_1.md
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@ -29,13 +29,13 @@ Where D is the rank of input tensor `data`. The axis being split must be evenly
**Inputs**
* **1**: `data`. A tensor of type `T` and arbitrary shape. **Required.**
* **2**: `axis`. Axis along `data` to split. A scalar of type `T_AXIS` within the range `[-rank(data), rank(data) - 1]`. Negative values address dimensions from the end. **Required.**
* **1**: `data`. A tensor of type *T* and arbitrary shape. **Required.**
* **2**: `axis`. Axis along `data` to split. A scalar of type *T_AXIS* within the range `[-rank(data), rank(data) - 1]`. Negative values address dimensions from the end. **Required.**
* **Note**: The dimension of input tensor `data` shape along `axis` must be evenly divisible by *num_splits* attribute.
**Outputs**
* **Multiple outputs**: Tensors of type `T`. The i-th output has the same shape as `data` input tensor except for dimension along `axis` which is `data.shape[axis]/num_splits`.
* **Multiple outputs**: Tensors of type *T*. The i-th output has the same shape as `data` input tensor except for dimension along `axis` which is `data.shape[axis]/num_splits`.
**Types**

8
docs/ops/movement/StridedSlice_1.md
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@ -50,15 +50,15 @@
**Inputs**:
* **1**: `data` - input tensor to be sliced of type `T` and arbitrary shape. **Required.**
* **1**: `data` - input tensor to be sliced of type *T* and arbitrary shape. **Required.**
* **2**: `begin` - 1D tensor of type `T_IND` with begin indexes for input tensor slicing. **Required.**
* **2**: `begin` - 1D tensor of type *T_IND* with begin indexes for input tensor slicing. **Required.**
Out-of-bounds values are silently clamped. If `begin_mask[i]` is `1`, the value of `begin[i]` is ignored and the range of the appropriate dimension starts from `0`. Negative values mean indexing starts from the end. For example, if `data=[1,2,3]`, `begin[0]=-1` means `begin[0]=3`.
* **3**: `end` - 1D tensor of type `T_IND` with end indexes for input tensor slicing. **Required.**
* **3**: `end` - 1D tensor of type *T_IND* with end indexes for input tensor slicing. **Required.**
Out-of-bounds values will be silently clamped. If `end_mask[i]` is `1`, the value of `end[i]` is ignored and the full range of the appropriate dimension is used instead. Negative values mean indexing starts from the end. For example, if `data=[1,2,3]`, `end[0]=-1` means `end[0]=3`.
* **4**: `stride` - 1D tensor of type `T_IND` with strides. **Optional.**
* **4**: `stride` - 1D tensor of type *T_IND* with strides. **Optional.**
**Types**
* *T*: any supported type.

4
docs/ops/movement/Tile_1.md
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@ -14,8 +14,8 @@ No attributes available.
**Inputs**:
* **1**: "data" - an input tensor to be padded. A tensor of type T1. **Required.**
* **2**: "repeats" - a per-dimension replication factor. For example, *repeats* equal to 88 means that the output tensor gets 88 copies of data from the specified axis. A tensor of type T2. **Required.**
* **1**: "data" - an input tensor to be padded. A tensor of type *T1*. **Required.**
* **2**: "repeats" - a per-dimension replication factor. For example, *repeats* equal to 88 means that the output tensor gets 88 copies of data from the specified axis. A tensor of type *T2*. **Required.**
**Outputs**:

6
docs/ops/movement/Transpose_1.md
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@ -14,12 +14,12 @@
**Inputs**:
* **1**: `arg` - the tensor to be transposed. A tensor of type `T` and arbitrary shape. **Required.**
* **2**: `input_order` - the permutation to apply to the axes of the first input shape. A 1D tensor of `n` elements `T_AXIS` type and shape `[n]`, where `n` is the rank of the first input or `0`. The tensor's value must contain every integer in the range `[0, n-1]`, but if an empty tensor is specified (shape `[0]`), then the axes will be inverted. **Required.**
* **1**: `arg` - the tensor to be transposed. A tensor of type *T* and arbitrary shape. **Required.**
* **2**: `input_order` - the permutation to apply to the axes of the first input shape. A 1D tensor of `n` elements *T_AXIS* type and shape `[n]`, where `n` is the rank of the first input or `0`. The tensor's value must contain every integer in the range `[0, n-1]`, but if an empty tensor is specified (shape `[0]`), then the axes will be inverted. **Required.**
**Outputs**:
* **1**: A tensor of type `T` and transposed shape according to the rules specified above.
* **1**: A tensor of type *T* and transposed shape according to the rules specified above.
**Types**

6
docs/ops/normalization/LRN_1.md
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@ -63,13 +63,13 @@ output = data / (bias + (alpha / size ** len(axes)) * sqr_sum) ** beta
**Inputs**
* **1**: `data` - tensor of type `T` and arbitrary shape. **Required.**
* **1**: `data` - tensor of type *T* and arbitrary shape. **Required.**
* **2**: `axes` - 1D tensor of type `T_IND` which specifies indices of dimensions in `data` which define normalization slices. **Required.**
* **2**: `axes` - 1D tensor of type *T_IND* which specifies indices of dimensions in `data` which define normalization slices. **Required.**
**Outputs**
* **1**: Output tensor of type `T` and the same shape as the `data` input tensor.
* **1**: Output tensor of type *T* and the same shape as the `data` input tensor.
**Types**
* *T*: any supported floating point type.

2
docs/ops/pooling/MaxPool_1.md
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@ -67,7 +67,7 @@
**Inputs**:
* **1**: 3D, 4D or 5D input tensor of type T. Required.
* **1**: 3D, 4D or 5D input tensor of type *T*. Required.
**Outputs**:
* **1**: Input shape can be either `[N, C, H]`, `[N, C, H, W]` or `[N, C, H, W, D]`. Then the corresponding output shape will be `[N, C, H_out]`, `[N, C, H_out, W_out]` or `[N, C, H_out, W_out, D_out]`. Output tensor has the same data type as input tensor.

6
docs/ops/sequence/CTCGreedyDecoder_1.md
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@ -28,13 +28,13 @@ Sequences in the batch can have different length. The lengths of sequences are c
**Inputs**
* **1**: `data` - input tensor with batch of sequences of type `T_F` and shape `[T, N, C]`, where `T` is the maximum sequence length, `N` is the batch size and `C` is the number of classes. **Required.**
* **1**: `data` - input tensor with batch of sequences of type *T_F* and shape `[T, N, C]`, where `T` is the maximum sequence length, `N` is the batch size and `C` is the number of classes. **Required.**
* **2**: `sequence_mask` - input tensor with sequence masks for each sequence in the batch of type `T_F` populated with values `0` and `1` and shape `[T, N]`. **Required.**
* **2**: `sequence_mask` - input tensor with sequence masks for each sequence in the batch of type *T_F* populated with values `0` and `1` and shape `[T, N]`. **Required.**
**Output**
* **1**: Output tensor of type `T_F` and shape `[N, T, 1, 1]` which is filled with integer elements containing final sequence class indices. A final sequence can be shorter that the size `T` of the tensor, all elements that do not code sequence classes are filled with `-1`.
* **1**: Output tensor of type *T_F* and shape `[N, T, 1, 1]` which is filled with integer elements containing final sequence class indices. A final sequence can be shorter that the size `T` of the tensor, all elements that do not code sequence classes are filled with `-1`.
**Types**
* *T_F*: any supported floating point type.

2
docs/ops/shape/Reshape_1.md
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@ -26,7 +26,7 @@ If `special_zero` is set to `true` index of `0` cannot be larger than the rank o
**Inputs**:
* **1**: `data` a tensor of type T and arbitrary shape. **Required**.
* **1**: `data` a tensor of type *T* and arbitrary shape. **Required**.
* **2**: `shape` 1D tensor of type *T_SHAPE* describing output shape. **Required**.