94 lines
2.3 KiB
Markdown
94 lines
2.3 KiB
Markdown
## LogicalAnd <a name="LogicalAnd"></a> {#openvino_docs_ops_logical_LogicalAnd_1}
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**Versioned name**: *LogicalAnd-1*
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**Category**: Logical binary operation
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**Short description**: *LogicalAnd* performs element-wise logical AND operation with two given tensors applying multi-directional broadcast rules.
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**Attributes**:
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* *auto_broadcast*
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* **Description**: specifies rules used for auto-broadcasting of input tensors.
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* **Range of values**:
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* *none* - no auto-broadcasting is allowed, all input shapes should match
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* *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>.
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* **Type**: string
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* **Default value**: "numpy"
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* **Required**: *no*
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**Inputs**
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* **1**: A tensor of type *T*. **Required.**
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* **2**: A tensor of type *T*. **Required.**
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**Outputs**
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* **1**: The result of element-wise logical AND operation. A tensor of type boolean.
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**Types**
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* *T*: boolean type.
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**Detailed description**
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Before performing logical operation, input tensors *a* and *b* are broadcasted if their shapes are different and `auto_broadcast` attributes is not `none`. Broadcasting is performed according to `auto_broadcast` value.
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After broadcasting *LogicalAnd* does the following with the input tensors *a* and *b*:
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\f[
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o_{i} = a_{i} and b_{i}
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\f]
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**Examples**
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*Example 1*
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```xml
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<layer ... type="LogicalAnd">
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<input>
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<port id="0">
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<dim>256</dim>
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<dim>56</dim>
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</port>
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<port id="1">
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<dim>256</dim>
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<dim>56</dim>
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</port>
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</input>
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<output>
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<port id="2">
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<dim>256</dim>
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<dim>56</dim>
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</port>
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</output>
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</layer>
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```
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*Example 2: broadcast*
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```xml
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<layer ... type="LogicalAnd">
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<input>
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<port id="0">
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<dim>8</dim>
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<dim>1</dim>
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<dim>6</dim>
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<dim>1</dim>
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</port>
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<port id="1">
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<dim>7</dim>
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<dim>1</dim>
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<dim>5</dim>
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</port>
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</input>
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<output>
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<port id="2">
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<dim>8</dim>
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<dim>7</dim>
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<dim>6</dim>
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<dim>5</dim>
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</port>
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</output>
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</layer>
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```
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