TF Lite Converter produces outputs in an incorrect order when multiple outputs are present
I was able to reproduce this issue. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/d67c52ee79ab0dcbbd9f8699bc8a8575/61065.ipynb).

@pkgoogle Could you please look into this issue?

Thanks.
 Hi @YaoJiayi,

Input/Output ordering is not guaranteed to be preserved during conversion. If you have multiple inputs or outputs in which the ordering matters, please use signatures to map tf inputs/outputs to tflite inputs/outputs: https://www.tensorflow.org/lite/guide/signatures

If you are satisfied with this answer, please feel free to close the issue. Thanks for the feedback!!! @pkgoogle  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/61065">Yes</a>
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### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 20.04
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.14.0-dev20230622

### 2. Code
This is the minimized code to reproduce the issue:
```python
import tensorflow as tf
import numpy as np

x1 = tf.constant([1.], shape=[1,1])

class Model(tf.keras.Model):
  def __init__(self):
    super(Model, self).__init__()

  def call(self, x1):
    x2 = tf.eye(1)
    x3 = tf.eye(2)
    x4 = tf.eye(1)
    return [x2, x3, x4]


m = Model()
expected_value = m(x1)

converter = tf.lite.TFLiteConverter.from_keras_model(m)
tflite_model = converter.convert()

def _evaluateTFLiteModel(tflite_model, input_data):
    interpreter = tf.lite.Interpreter(model_content=tflite_model)
    interpreter.allocate_tensors()

    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    for i in range(len(input_data)):
        interpreter.set_tensor(input_details[i]['index'], input_data[i])

    interpreter.invoke()

    output_data = [interpreter.get_tensor(output_details[i]['index'])
                   for i in range(len(output_details))]
    return output_data

actual_value = _evaluateTFLiteModel(tflite_model,[x1])

#Outputs
print(f"Expected output_1: {expected_value[0].numpy()}")
print(f"Lite output_1: {actual_value[0]}")
print("-----------------------------------")
print(f"Expected output_2: {expected_value[1].numpy()}")
print(f"Lite output_2: {actual_value[1]}")
print("-----------------------------------")
print(f"Expected output_3: {expected_value[2].numpy()}")
print(f"Lite output_3: {actual_value[2]}")
#wrong order

tf.lite.experimental.Analyzer.analyze(model_content=tflite_model) #Output IR
```
### 3. Failure after conversion
Output (incorrect order):
```
Expected output_1: [[1.]]
Lite output_1: [[1.]]
-----------------------------------
Expected output_2: [[1. 0.]
 [0. 1.]]
Lite output_2: [[1.]]
-----------------------------------
Expected output_3: [[1.]]
Lite output_3: [[1. 0.]
 [0. 1.]]
```
Lite IR:
```
Subgraph#0 main(T#0) -> [T#1, T#1, T#2]

Tensors of Subgraph#0
  T#0(serving_default_input_1:0) shape_signature:[-1, 1], type:FLOAT32
  T#1(PartitionedCall:0) shape:[1, 1], type:FLOAT32 RO 4 bytes, buffer: 2, data:[1]
  T#2(PartitionedCall:1) shape:[2, 2], type:FLOAT32 RO 16 bytes, buffer: 3, data:[1, 0, 0, 1]
```
TF Lite converter produces wrong outputs:
- In the Lite IR, the ouput should be `[T#1, T#2, T#1]`   instead of `[T#1, T#1, T#2]` .

