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GRN spec revision (#6666)

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* Update detailed description

* Update attrs description

* Add Types section and Inputs Outputs description

* Update example
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docs/ops/normalization/GRN_1.md
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**Detailed description**: **Detailed description**:
*GRN* computes the L2 norm by channels for input tensor with shape `[N, C, ...]`. *GRN* does the following with the input tensor: *GRN* computes the L2 norm across channels for input tensor with shape `[N, C, ...]`. *GRN* does the following with the input tensor:
output[i0, i1, ..., iN] = x[i0, i1, ..., iN] / sqrt(sum[j = 0..C-1](x[i0, j, ..., iN]**2) + bias) output[i0, i1, ..., iN] = x[i0, i1, ..., iN] / sqrt(sum[j = 0..C-1](x[i0, j, ..., iN]**2) + bias)
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* *bias* * *bias*
* **Description**: *bias* is added to the variance. * **Description**: *bias* is added to the sum of squares.
* **Range of values**: a non-negative floating-point value * **Range of values**: a positive floating-point number
* **Type**: `float` * **Type**: `float`
* **Required**: *yes* * **Required**: *yes*
**Inputs** **Inputs**
* **1**: Input tensor with element of any floating-point type and `2 <= rank <=4`. **Required.** * **1**: `data` - A tensor of type *T* and `2 <= rank <= 4`. **Required.**
**Outputs** **Outputs**
* **1**: Output tensor of the same type and shape as the input tensor. * **1**: The result of *GRN* function applied to `data` input tensor. Normalized tensor of the same type and shape as the data input.
**Types**
* *T*: arbitrary supported floating-point type.
**Example** **Example**
```xml ```xml
<layer id="5" name="normalization" type="GRN"> <layer ... type="GRN">
<data bias="1e-4"/> <data bias="1e-4"/>
<input> <input>
<port id="0"> <port id="0">