Is it possible to have quantised fused_tanh (with add op ) via TFlite converter

System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
  Linux Ubuntu 18.04
• TensorFlow installed from (source or binary):
  Source
• TensorFlow version (or github SHA if from source):
  2.6.0

Provide the text output from tflite_convert

When comparing against the quantized Keras output. The TfLite output does not match the Keras output.

Standalone code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.

https://colab.research.google.com/drive/1IqfO1FTKBahe9iahqD4ZgjGnkV1Utulk?usp=sharing

Also, please include a link to a GraphDef or the model if possible.

Any other info / logs

Include any logs or source code that would be helpful to diagnose the problem.
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should be attached.

Is it possible to have an ADD operator with a fused TANH activation in TFL ? Such quantized int16 model is producing wrong results compare to Keras output ?
Model:
=====
input = tf.keras.layers.Input(shape=  (1, 128, 3), batch_size=1)
add = tf.keras.layers.Add()([input, input])
act = tf.keras.layers.Activation(tf.keras.activations.tanh)(add)
model = tf.keras.Model(inputs=input, outputs= act)
=======


```From the schema it seems it's supported:
`
table AddOptions
{ fused_activation_function:ActivationFunctionType; // Parameters supported by version 3. pot_scale_int16:bool = true;}

enum ActivationFunctionType : byte {
NONE = 0,
RELU = 1,
RELU_N1_TO_1 = 2,
RELU6 = 3,
TANH = 4,
SIGN_BIT = 5,
}
`

But MLIR converter currently only fuses TFL_ReluOp, TFL_Relu6Op and TFL_Relu1Op:
https://github.com/tensorflow/tensorflow/blob/77d8c333405a080c57850c45531dbbf077b2bd0e/tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td#L86
https://github.com/tensorflow/tensorflow/blob/77d8c333405a080c57850c45531dbbf077b2bd0e/tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td#L429

Can you confirm if it's possible to fuse 16-bit tanh in quantised model ?

Start with the linked Colab reproduction and compare the quantized TFLite output with the Keras output for the int16 Add-plus-Tanh model. Read tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td at the referenced lines and the AddOptions schema to determine whether fused TANH is supported; done means the support or failure mode is confirmed with reproducible results.