NotImplementedError while converting a tensorflow model to coreml using coremltools
Hi @lidamsoukaina ,

At present all the TF Ops are not implemented to workable on TFlite. Hence you are getting this error.  @lidamsoukaina ,

Please find the list of Ops supported with TFlite [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/builtin_ops.h). @SuryanarayanaY I am not facing this problem while converting to tflite. In fact the conversion to tflite is well done.
I have this issue with the conversion to coreml. Hi @lidamsoukaina, this appears to be an issue with coreml? It does not appear to touch TFLite code, I think you may have better luck with their code base? This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61509">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61509">No</a>

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

2.13.0

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I am facing a NotImplementedError while trying to convert [MoViNets](https://github.com/tensorflow/models/tree/master/official/projects/movinet) model to coreml.

The model is saved in [SavedModel](https://www.tensorflow.org/guide/saved_model) format and I am using a tensorflow version equal to **2.13.0** and a version of [Core ML Tools](https://coremltools.readme.io/docs/what-are-coreml-tools) equal to **6.3.0** and a **3.10.6** python.

I don't understand the origin of **StatefulPartitionedCall** operation and its meaning, any idea what's could be going on?

### Here are the steps to reproduce the error:
1. Download a savedModel from the following [link](https://tfhub.dev/tensorflow/movinet/a0/base/kinetics-600/classification/3).
2. Convert the model using:
```shell
import coremltools as ct
coreml_model = ct.convert(saved_dir, convert_to="mlprogram")
```
where *saved_dir* is the path to the downloaded model.

### Relevant log output

```shell
NotImplementedError: Conversion for TF op 'StatefulPartitionedCall' not implemented.
 
name: "StatefulPartitionedCall"
op: "StatefulPartitionedCall"
input: "image"
input: "unknown"
input: "unknown_0"
input: "unknown_1"
input: "unknown_2"
input: "unknown_3"
input: "unknown_4"
input: "unknown_5"
input: "unknown_6"
input: "unknown_7"
input: "unknown_8"
input: "unknown_9"
input: "unknown_10"
input: "unknown_11"
input: "unknown_12"
input: "unknown_13"
input: "unknown_14"
input: "unknown_15"
...
input: "unknown_597"
input: "unknown_598"
input: "unknown_599"
attr {
  key: "Tin"
  value {
    list {
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      type: DT_FLOAT
      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: "_has_manual_control_dependencies"
  value {
    b: true
  }
}
attr {
  key: "_read_only_resource_inputs"
  value {
    list {
      i: 1
      i: 2
      i: 3
      i: 4
      i: 5
      i: 6
      i: 7
      i: 8
      i: 9
      i: 10
      i: 11
      i: 12
      i: 13
      i: 14
      i: 15
...
      i: 597
      i: 598
      i: 599
      i: 600
      i: 601
    }
  }
}
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_predict_frozen_288748"
    }
  }
}

```
```
