Flowmode
Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).

View this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60614/checks?check_run_id=13544445685) of the CLA check for more information.

For the most up to date status, view the checks section at the bottom of the pull request.
``Converting model...
Model converted successfully.

Standalone code to reproduce the issue: 

import tensorflow as tf

def my_function(x):
  return tf.add(x, 1)

model = tf.keras.models.Sequential([
  tf.keras.layers.Input(shape=(1)),
  tf.keras.layers.Dense(1, activation='relu'),
  my_function,
])

model.compile(optimizer='adam', loss='mse')

model.fit(x=[1, 2, 3], y=[2, 3, 4], epochs=10)

tflite_model = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model.experimental_generate_debug_info = True
tflite_model.target_spec.supported_ops = [
  tflite.OpsSet.TFLITE_BUILTIN,
  tflite.OpsSet.SELECT_TF_OPS,
]
tflite_model.output_format = tflite.Model.ModelFormat.TFLITE
tflite_model.experimental_enable_mlir_converter = True
tflite_model.experimental_enable_quantization = True
tflite_model.inference_input_type = tflite.TensorType.FLOAT32
tflite_model.inference_output_type = tflite.TensorType.FLOAT32
tflite_model.input_tensors[0].name = 'x'
tflite_model.output_tensors[0].name = 'y'
tflite_model.output_file = 'my_model.tflite'

tflite_model.convert()

> `print(tflite_model.output_file)`

> This code will create a TensorFlow Lite model and save it to a file called my_model.tflite.

Any other info / logs:

There are no other logs or source code that would be helpful to diagnose the problem. `