[MLIR] tf-mlir-translate not working for keras sequential model
If you can reproduce the issue, please comment if there are any command line mitigation for that. I would really not like to rewrite existing models using tf.function. @monowaranjum,
You need to create a wrapper [like this](https://github.com/tensorflow/tensorflow/blob/b41bee7adb423973821d1b93193b8becbddead56/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/keras.py#L36) and save it (with the wrapper, you specify an explicit @tf.function input_signature=, as the error was suggesting). 

Then, when you load it, you need to specify "exported names" for just the functions in your wrapper (in the case of the link I specified, it is just one function "predict").

So after saving the wrapper tf.Module you would load it with something like this:

`tf-mlir-translate --savedmodel-objectgraph-to-mlir --tf-savedmodel-exported-names=predict /path/to/tf2_model -o out.mlir`

(I notice that you are using a somewhat out of date version of tf-mlir-translate; you should be able to find the corresponding flags in your version; I think the -savedmodel-to-mlir -> -savedmodel-objectgraph-to-mlir change is the only non-obvious renaming)

We also have a Python API that does the same. You can find how it is invoked in [common.py](https://github.com/tensorflow/tensorflow/blob/b9fce9ec2edede6ccce183f0dca6e89162371388/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/common.py#L87) which underlies the keras test I linked above.

Thank you! Thanks, it worked. 
### Issue type

Documentation Feature Request

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

Yes

### Source

source

### TensorFlow version

2.15

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.10

### Bazel version

5.1.1

### GCC/compiler version

11.4

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I wrote a small model to test a few compiler features in tensorflow. The model is saved to a directory using ```SavedModel``` format. Then I tried using ```tf-mlir-translate``` to convert the model into tf MLIR code. However, that did not work. The conversion terminated abruptly while giving the error message that function with multiple signature detected. I inspected the model with ```saved-model-cli``` and found that indeed that ```__call__``` function has two concrete functions with two different set of parameters. I have no idea why the ```__call__``` function has two concrete instantiations. Especially when I never really have any control over how the internal functions get instantiated except for ```model.fit()``` call. 

I expected the MLIR file to be generated. But instead, it is continuously showing error about multiple concrete functions.

### Standalone code to reproduce the issue

```shell
Here is the model code: 


import tensorflow as tf
import numpy as np


def get_linear_classifier():
    model = tf.keras.Sequential()    
    model.add(tf.keras.layers.Input(shape = (784)))
    model.add(tf.keras.layers.Dense(32, activation = 'relu'))
    model.add(tf.keras.layers.Dense(10))
    model.summary()
    return model


def dense_train(model, batch_num, train_set):
    train_examples = train_set['data']/255.0
    train_labels = train_set['label']
    num_examples = train_examples.shape[0]
    image_width = train_examples.shape[1]
    image_height = train_examples.shape[2]
    train_examples = np.reshape(train_examples, (num_examples, image_width * image_height))
    temp = np.zeros((60000, 10))
    for i in range(len(train_labels)):
        temp[i][train_labels[i]] = 1.0
        
    print("Shapes: {} {}".format(train_examples.shape, temp.shape))
    
    history = model.fit(train_examples, temp, batch_size = 100, epochs = 40)    
    
    return history
    
    
tf.random.set_seed(42)
tf.keras.backend.clear_session()

model = get_linear_classifier()

(train_x, train_y), (test_x, test_y) = tf.keras.datasets.mnist.load_data()
        
model.compile( optimizer= tf.keras.optimizers.Adam(learning_rate=0.01), loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.CategoricalAccuracy()])
train_data = {'data': train_x, 'label': train_y}
training_history = dense_train(model, 100, train_data)

model.save('/path/to/working/directory/graph-artifacts/toy_model1')

```


Then I ran the following command: 

```
tf-mlir-translate /path/to/working/directory/graph-artifacts/toy_model1 --savedmodel-objectgraph-to-mlir
```

I got the following error message: 

```
tensorflow/compiler/mlir/tensorflow/translate/tf_mlir_translate.cc:172] SavedModel import failed: INVALID_ARGUMENT: Exported function with exported name(s) '__call__', 'keras_api.__call__' with multiple concrete functions. Add @tf.function(input_signature=[...]) on this function, or use a narrower list of exported names that excludes this function.

```
```


### Relevant log output

```shell
saved-model-cli shows the following output after running the command: saved_model_cli show --dir=/home/rashik/Documents/tensorfow_mlir_test/TF-ESRGAN/graph-artifacts/toy_model1 --all


Output:
W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is: 

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['input_1'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 784)
        name: serving_default_input_1:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['dense_1'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 10)
        name: StatefulPartitionedCall:0
  Method name is: tensorflow/serving/predict
The MetaGraph with tag set ['serve'] contains the following ops: {'ShardedFilename', 'ReadVariableOp', 'BiasAdd', 'StringJoin', 'SaveV2', 'VarHandleOp', 'NoOp', 'Select', 'AssignVariableOp', 'RestoreV2', 'Const', 'MergeV2Checkpoints', 'StatefulPartitionedCall', 'StaticRegexFullMatch', 'Identity', 'DisableCopyOnRead', 'Relu', 'MatMul', 'Placeholder', 'Pack'}

Concrete Functions:
  Function Name: '__call__'
    Option #1
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 784), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 784), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None

  Function Name: '_default_save_signature'
    Option #1
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 784), dtype=tf.float32, name='input_1')

  Function Name: 'call_and_return_all_conditional_losses'
    Option #1
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 784), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 784), dtype=tf.float32, name='input_1')
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None
```
