TF Movinet - TF op 'StatefulPartitionedCall' not implemented.

Hi! I am currently using Movinet Model from the Tensorflow Hub. When I tried to convert it using the coremltools, I ran into error: NotImplementedError: Conversion for TF op 'StatefulPartitionedCall' not implemented.
Can you help me to find a way round to solve this problem, please?
Here's the error log I got:
Running TensorFlow Graph Passes: 100%|██████████| 6/6 [00:01<00:00, 5.24 passes/s]
Converting TF Frontend ==> MIL Ops: 44%|████▍ | 513/1160 [00:00<00:00, 3003.02 ops/s]

```
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
[](https://localhost:8080/#) in
1 # model.summary()
----> 2 ct.convert('/content/A0_tuned_3-4', convert_to="mlprogram")

[/usr/local/lib/python3.7/dist-packages/coremltools/converters/mil/frontend/tensorflow/convert_utils.py](https://localhost:8080/#) in convert_graph(context, graph, outputs)
186 node.op, node.original_node
187 )
--> 188 raise NotImplementedError(msg)
189 add_op(context, node)
190

NotImplementedError: Conversion for TF op 'StatefulPartitionedCall' not implemented.

name: "StatefulPartitionedCall/movinet_classifier_1/movinet/stem/stem/conv3d/StatefulPartitionedCall"
op: "StatefulPartitionedCall"
input: "image"
input: "Func/StatefulPartitionedCall/input/_1"
attr {
key: "Tin"
value {
list {
type: DT_FLOAT
type: DT_FLOAT
}
}
}
attr {
key: "Tout"
value {
list {
type: DT_FLOAT
}
}
}
attr {
key: "_XlaMustCompile"
value {
b: true
}
}
attr {
key: "_collective_manager_ids"
value {
list {
}
}
}
attr {
key: "_read_only_resource_inputs"
value {
list {
i: 1
}
}
}
attr {
key: "config"
value {
s: ""
}
}
attr {
key: "config_proto"
value {
s: "\n\007\n\003CPU\020\001\n\007\n\003GPU\020\0012\005*\0010J\0008\001\202\001\000"
}
}
attr {
key: "executor_type"
value {
s: ""
}
}
attr {
key: "f"
value {
func {
name: "__inference_call_frozen_188259"
}
}
}
```

and here's how I did it:
```
num_classes = 100
batch_size = 8
num_frames = 16
frame_stride = 10
resolution = 172
model_id = 'a3'

tf.keras.backend.clear_session()

backbone = movinet.Movinet(model_id=model_id)
model = movinet_model.MovinetClassifier(backbone=backbone, num_classes=600)
model.build([1, 1, 1, 1, 3])

# Load pretrained weights
!wget https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a3_base.tar.gz -O movinet_a3_base.tar.gz -q
!tar -xvf movinet_a3_base.tar.gz

checkpoint_dir = 'movinet_a3_base'
checkpoint_path = tf.train.latest_checkpoint(checkpoint_dir)
checkpoint = tf.train.Checkpoint(model=model)
status = checkpoint.restore(checkpoint_path)
status.assert_existing_objects_matched()

def build_classifier(backbone, num_classes, freeze_backbone=False):
"""Builds a classifier on top of a backbone model."""
model = movinet_model.MovinetClassifier(
backbone=backbone,
num_classes=num_classes)
model.build([batch_size, num_frames, resolution, resolution, 3])

if freeze_backbone:
for layer in model.layers[:-1]:
layer.trainable = False
model.layers[-1].trainable = True

return model

# Wrap the backbone with a new classifier to create a new classifier head
# with num_classes outputs (101 classes for UCF101).
# Freeze all layers except for the final classifier head.
model = build_classifier(backbone, num_classes, freeze_backbone=True)
ct.convert(model, convert_to="mlprogram")
```

## System environment (please complete the following information):
- coremltools version: 6.0
- Using Google Colab
- Tensorflow 2.10.0

The traceback points to convert_utils.py in the TensorFlow MIL frontend; start there and reproduce the failure with the supplied Movinet model and ct.convert call. Done means the model conversion no longer raises the unsupported StatefulPartitionedCall error.

Written by the indexing model from the issue text.