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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**:
*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)
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* *bias*
* **Description**: *bias* is added to the variance.
* **Range of values**: a non-negative floating-point value
* **Description**: *bias* is added to the sum of squares.
* **Range of values**: a positive floating-point number
* **Type**: `float`
* **Required**: *yes*
**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**
* **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**
```xml
<layer id="5" name="normalization" type="GRN">
<layer ... type="GRN">
<data bias="1e-4"/>
<input>
<port id="0">