.. _op_ai_onnx_And:

And
===

- **Domain**: ``ai.onnx``
- **Since version**: 7

Returns the tensor resulted from performing the ``And`` logical operation
elementwise on the input tensors ``A`` and ``B`` (with Numpy-style broadcasting support).

**Inputs**

- **A** (*T*): First input operand for the logical operator.
- **B** (*T*): Second input operand for the logical operator.

**Outputs**

- **C** (*T1*): Result tensor.

**Type Constraints**

- **T**: Constrain input to boolean tensor.
  Allowed types: tensor(bool).
- **T1**: Constrain output to boolean tensor.
  Allowed types: tensor(bool).

Examples
--------

**test_cc_and**

.. code-block:: text

    Inputs:
      x: shape=(2, 2), dtype=bool
        [[ True, False],
         [ True, False]]
      y: shape=(2, 2), dtype=bool
        [[ True,  True],
         [False, False]]

    Outputs:
      z: shape=(2, 2), dtype=bool
        [[ True, False],
         [False, False]]

**test_cc_and_bcast**

.. code-block:: text

    Inputs:
      x: shape=(2, 2), dtype=bool
        [[ True, False],
         [ True, False]]
      y: shape=(), dtype=bool
        True

    Outputs:
      z: shape=(2, 2), dtype=bool
        [[ True, False],
         [ True, False]]

Differences with previous version (1)
-------------------------------------

**SchemaDiff**: ``And`` (domain ``'ai.onnx'``)

* old version: 1
* new version: 7
* breaking: no

**Documentation:**

* line similarity: 0.40 (+1/-5 lines)

.. code-block:: diff

    --- And v1
    +++ And v7
    @@ -1,7 +1,3 @@
     
     Returns the tensor resulted from performing the `And` logical operation
    -elementwise on the input tensors `A` and `B`.
    -
    -If broadcasting is enabled, the right-hand-side argument will be broadcasted
    -to match the shape of left-hand-side argument. See the doc of `Add` for a
    -detailed description of the broadcasting rules.
    +elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).

Version History
---------------

- :doc:`Version 1 <And-1>`