# tf.raw_ops.QuantizedMatMulWithBiasAndRelu

<br />

Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu fusion.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.QuantizedMatMulWithBiasAndRelu`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedMatMulWithBiasAndRelu)

<br />

    tf.raw_ops.QuantizedMatMulWithBiasAndRelu(
        a,
        b,
        bias,
        min_a,
        max_a,
        min_b,
        max_b,
        Toutput=https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/dtypes#qint32,
        transpose_a=False,
        transpose_b=False,
        input_quant_mode='MIN_FIRST',
        name=None
    )

The inputs must be two-dimensional matrices and 1D bias vector. And the inner
dimension of `a` (after being transposed if `transpose_a` is non-zero) must
match the outer dimension of `b` (after being transposed if `transposed_b` is
non-zero). Then do broadcast add operation with bias values on the matrix
multiplication result. The bias size must match inner dimension of `b`. Then do
relu activation to get non-negative result.

Args:
a: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
A matrix to be multiplied. Must be a two-dimensional tensor of type `quint8`.
b: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
A matrix to be multiplied and must be a two-dimensional tensor of type `qint8`.
bias: A `Tensor` of type `float32`.
A 1D bias tensor with size matching with inner dimension of `b` (after being
transposed if `transposed_b` is non-zero).
min_a: A `Tensor` of type `float32`.
The float value that the lowest quantized `a` value represents.
max_a: A `Tensor` of type `float32`.
The float value that the highest quantized `a` value represents.
min_b: A `Tensor` of type `float32`.
The float value that the lowest quantized `b` value represents.
max_b: A `Tensor` of type `float32`.
The float value that the highest quantized `b` value represents.
Toutput: An optional [`tf.DType`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/dtypes/DType) from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to [`tf.qint32`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf#qint32).
transpose_a: An optional `bool`. Defaults to `False`.
If true, `a` is transposed before multiplication.
transpose_b: An optional `bool`. Defaults to `False`.
If true, `b` is transposed before multiplication.
input_quant_mode: An optional `string` from: `"MIN_FIRST", "SCALED"`. Defaults to `"MIN_FIRST"`.
Input data quantization mode. Either MIN_FIRST(default) or SCALED.
name: A name for the operation (optional).

Returns:
A tuple of `Tensor` objects (out, min_out, max_out).

    out: A `Tensor` of type `Toutput`.
    min_out: A `Tensor` of type `float32`.
    max_out: A `Tensor` of type `float32`.