## Broadcast <a name="Broadcast"></a> {#openvino_docs_ops_movement_Broadcast_1}

**Versioned name**: *Broadcast-1*

**Category**: Data movement

**Short description**: *Broadcast* replicates data on the first input to fit a given shape on the second input.

**Detailed description**:

*Broadcast* takes the first tensor `data` and, following broadcasting rules that are specified by `mode` attribute and the 3rd input `axes_mapping`, builds a new tensor with shape matching the 2nd input tensor `target_shape`. `target_shape` input is a 1D integer tensor that represents required shape of the output.

Attribute `mode` and the 3rd input `axes_mapping` are relevant for cases when rank of the input `data` tensor doesn't match the size of the `target_shape` input. They both define how axes from `data` shape are mapped to the output axes. If `mode` is set to `numpy`, it means that the standard one-directional numpy broadcasting rules are applied. They are similar to rules that applied in all binary element-wise operations in case when `auto_broadcasting` attribute is set to `numpy`, and are similar to rules described at [here](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html#general-broadcasting-rules), when only one-directional broadcasting is applied: input tensor `data` is broadcasted to `target_shape` but not vice-versa.

In case if `mode` is set to `explicit`, then 3rd input `axes_mapping` comes to play. It contains a list of axis indices, each index maps an axis from the 1st input tensor `data` to axis in the output. The size of `axis_mapping` should match the rank of input `data` tensor, so all axes from `data` tensor should be mapped to axes of the output.

For example, `axes_mapping = [1]` enables broadcasting of a tensor with shape `[C]` to shape `[N,C,H,W]` by replication of initial tensor along dimensions 0, 2 and 3. Another example is broadcasting of tensor with shape `[H,W]` to shape `[N,H,W,C]` with `axes_mapping = [1, 2]`. Both examples requires `mode` set to `explicit` and providing mentioned `axes_mapping` input, because such operations cannot be expressed with `axes_mapping` set to `numpy`.


**Attributes**:

* *mode*

  * **Description**: specifies rules used for mapping of `input` tensor axes to output shape axes.
  * **Range of values**:
    * *numpy* - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in <a href="https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md">ONNX docs</a>.; only one-directional broadcasting is applied from `data` to `target_shape`. If this attribute value is used, then the 3rd input for the operation shouldn't be provided.
    * *explicit* - mapping of the input `data` shape axes to output shape is provided as an explicit 3rd input.
  * **Type**: string
  * **Default value**: "numpy"
  * **Required**: *no*


**Inputs**:

*   **1**: `data` - source tensor of any type and shape that is being broadcasted. Required.

*   **2**: `taget_shape` - 1D integer tensor describing output shape. Required.

*   **3**: `axes_mapping` - 1D integer tensor describing a list of axis indices, each index maps an axis from the 1st input tensor `data` to axis in the output. The index values in this tensor should be sorted, that disables on-the-fly transpositions of input `data` tensor while the broadcasting. `axes_mapping` input is optional depending on `mode` value.

**Outputs**:

*   **1**: Output tensor with replicated content from the 1st tensor `data` and with shape matched `target_shape`.

**Example**

```xml
<layer ... type="Broadcast" ...>
    <data mode="numpy"/>
    <input>
        <port id="0">
            <dim>16</dim>
            <dim>1</dim>
            <dim>1</dim>
       </port>
        <port id="1">
            <dim>4</dim>   <!--The tensor contains 4 elements: [1, 16, 50, 50] -->
        </port>
        <!-- the 3rd input shouldn't be provided with mode="numpy" -->
    </input>
    <output>
        <port id="2">
            <dim>1</dim>
            <dim>16</dim>
            <dim>50</dim>
            <dim>50</dim>
        </port>
    </output>
</layer>

<layer ... type="Broadcast" ...>
    <data mode="explicit"/>
    <input>
        <port id="0">
            <dim>16</dim>
       </port>
        <port id="1">
            <dim>4</dim>   <!--The tensor contains 4 elements: [1, 16, 50, 50] -->
        </port>
        <port id="1">
            <dim>1</dim>   <!--The tensor contains 1 elements: [1] -->
        </port>
    </input>
    <output>
        <port id="2">
            <dim>1</dim>
            <dim>16</dim>
            <dim>50</dim>
            <dim>50</dim>
        </port>
    </output>
</layer>

<layer ... type="Broadcast" ...>
    <data mode="explicit"/>
    <input>
        <port id="0">
            <dim>50</dim>
            <dim>50</dim>
       </port>
        <port id="1">
            <dim>4</dim>   <!--The tensor contains 4 elements: [1, 50, 50, 16] -->
        </port>
        <port id="1">
            <dim>2</dim>   <!--The tensor contains 2 elements: [1, 2] -->
        </port>
    </input>
    <output>
        <port id="2">
            <dim>1</dim>
            <dim>50</dim>
            <dim>50</dim>
            <dim>16</dim>
        </port>
    </output>
</layer>
```
