KeyError for the tensorflow module with the latest iree compiler

What happened?

Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/saved_model/function_serialization.py", line 67, in serialize_concrete_function bound_inputs.append(node_ids[capture])

  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/util/object_identity.py", line 136, in __getitem__
    return self._storage[self._wrap_key(key)]
           ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^
KeyError: <_ObjectIdentityWrapper wrapping <tf.Tensor: shape=(), dtype=resource, value=<ResourceHandle(name="SGD/learning_rate/5", device="/job:localhost/replica:0/task:0/device:CPU:0", container="Anonymous", type="tensorflow::Var", dtype and shapes : "[ DType enum: 1, Shape: [] ]")>>>

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/lingzhi/_Code/KCLOpenSource/kcl/a.py", line 68, in <module>
    vm_flatbuffer = iree.compiler.tf.compile_module(
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/iree/compiler/tools/tf.py", line 184, in compile_module
    return do_it(td)
           ^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/iree/compiler/tools/tf.py", line 177, in do_it
    tf.saved_model.save(module, saved_model_dir, options=options)
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/saved_model/save.py", line 1432, in save
    save_and_return_nodes(obj, export_dir, signatures, options)
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/saved_model/save.py", line 1467, in save_and_return_nodes
    _build_meta_graph(obj, signatures, options, meta_graph_def))
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/saved_model/save.py", line 1682, in _build_meta_graph
    return _build_meta_graph_impl(obj, signatures, options, meta_graph_def)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/saved_model/save.py", line 1635, in _build_meta_graph_impl
    object_graph_proto = _serialize_object_graph(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/saved_model/save.py", line 1155, in _serialize_object_graph
    serialized = function_serialization.serialize_concrete_function(
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/saved_model/function_serialization.py", line 69, in serialize_concrete_function
    raise KeyError(
KeyError: 'Failed to add concrete function \'b\'__inference_learn_121\'\' to object-based SavedModel as it captures tensor <tf.Tensor: shape=(), dtype=resource, value=<ResourceHandle(name="SGD/learning_rate/5", device="/job:localhost/replica:0/task:0/device:CPU:0", container="Anonymous", type="tensorflow::Var", dtype and shapes : "[ DType enum: 1, Shape: [] ]")>> which is unsupported or not reachable from root. One reason could be that a stateful object or a variable that the function depends on is not assigned to an attribute of the serialized trackable object (see SaveTest.test_captures_unreachable_variable).'

### Steps to reproduce your issue

1. Write the code (main.py) and setup the dependencies (the iree-compiler version is 20240228.815, os is macos arm64, and python version is 3.11)
```python
# python3 -m pip install matplotlib
# python3 -m pip install --upgrade tf-nightly  # Needed for stablehlo export in TF>=2.14
# python3 -m pip install iree-compiler iree-runtime iree-tools-tf

from matplotlib import pyplot as plt
import numpy as np
import tensorflow as tf
import iree.compiler.tf
import iree.runtime

tf.random.set_seed(91)
np.random.seed(91)
NUM_CLASSES = 10
NUM_ROWS, NUM_COLS = 28, 28
BATCH_SIZE = 32

class TrainableDNN(tf.Module):
    def __init__(self):
        super().__init__()

        # Create a Keras model to train.
        inputs = tf.keras.layers.Input((NUM_COLS, NUM_ROWS, 1))
        x = tf.keras.layers.Flatten()(inputs)
        x = tf.keras.layers.Dense(128)(x)
        x = tf.keras.layers.Activation("relu")(x)
        x = tf.keras.layers.Dense(10)(x)
        outputs = tf.keras.layers.Softmax()(x)
        self.model = tf.keras.Model(inputs, outputs)

        # Create a loss function and optimizer to use during training.
        self.loss = tf.keras.losses.SparseCategoricalCrossentropy()
        self.optimizer = tf.keras.optimizers.SGD(learning_rate=1e-2)

    @tf.function(
        input_signature=[tf.TensorSpec([BATCH_SIZE, NUM_ROWS, NUM_COLS, 1])]  # inputs
    )
    def predict(self, inputs):
        return self.model(inputs, training=False)

    # We compile the entire training step by making it a method on the model.
    @tf.function(
        input_signature=[
            tf.TensorSpec([BATCH_SIZE, NUM_ROWS, NUM_COLS, 1]),  # inputs
            tf.TensorSpec([BATCH_SIZE], tf.int32),  # labels
        ]
    )
    def learn(self, inputs, labels):
        # Capture the gradients from forward prop...
        with tf.GradientTape() as tape:
            probs = self.model(inputs, training=True)
            loss = self.loss(labels, probs)

        # ...and use them to update the model's weights.
        variables = self.model.trainable_variables
        gradients = tape.gradient(loss, variables)
        self.optimizer.apply_gradients(zip(gradients, variables))
        return loss

# ------------------------------
# Compile the Model with IREE
# ------------------------------

exported_names = ["predict", "learn"]
backend_choice = "llvm-cpu (CPU)"  # @param [ "vmvx (CPU)", "llvm-cpu (CPU)", "vulkan-spirv (GPU/SwiftShader – requires additional drivers) " ]
backend_choice = backend_choice.split(" ")[0]
# Compile the TrainableDNN module
vm_flatbuffer = iree.compiler.tf.compile_module(
    TrainableDNN(), target_backends=[backend_choice], exported_names=exported_names
)
backend_choice = "llvm-cpu (CPU)"  # @param [ "vmvx (CPU)", "llvm-cpu (CPU)", "vulkan-spirv (GPU/SwiftShader – requires additional drivers) " ]
backend_choice = backend_choice.split(" ")[0]
compiled_model = iree.runtime.load_vm_flatbuffer(vm_flatbuffer, backend=backend_choice)

```
2. Run the command `python3 main.py` and see the error message.

### What component(s) does this issue relate to?

_No response_

### Version information

_No response_

### Additional context

_No response_
iree-org / iree

KeyError for the tensorflow module with the latest iree compiler #16617

What happened?
