`Bias` fails to broadcast in the context of `matmul` in tf lite model
I was able reproduce this issue. Please find the [gist](https://colab.research.google.com/gist/pjpratik/98e2a454aafb91de2aa202bd1b7d9b2f/60929.ipynb).

@pkgoogle Could you please check if it is intended behaviour.

Thanks. Hello,

broadcasting is working for some simpler cases: [gist](https://colab.sandbox.google.com/gist/pkgoogle/35a12ccd0f2fca2ae8892b50db4260cd/60929.ipynb)

So this appears to be a legitimate bug.

@miaout17, can you please take a look? Thanks. Any update on this?
### 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-dev20230602

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

x1 = tf.constant([1., 2.], shape=[1, 2])

class Model(tf.keras.Model):
  def __init__(self):
    super(Model, self).__init__()
    self.w = tf.Variable([[3., 4.], [5., 6.]])
    self.b = tf.Variable([3.])
  @tf.function(input_signature=[tf.TensorSpec(x1.shape, x1.dtype)])
  def call(self, x):
    return tf.matmul(x, self.w) + self.b


m = Model()
print('Keras mode output: ', m(x1).numpy())

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

print('Lite mode output: ', _evaluateTFLiteModel(tflite_model,[x1])[0])

```
### 3. Failure after conversion
Output:
```
Keras mode output: [[16. 19.]]
Lite mode output:  RuntimeError: tensorflow/lite/kernels/fully_connected.cc:360 NumElements(bias) != SizeOfDimension(filter, 0) (1 != 2)Node number 0 (FULLY_CONNECTED) failed to prepare.Failed to apply the default TensorFlow Lite delegate indexed at 0.
```
Conversion Failure:
- During conversion, the model fails due to the following check at `tensorflow/lite/kernels/fully_connected.cc:360`
```cpp
if (bias) {
    TF_LITE_ENSURE_EQ(context, NumElements(bias), SizeOfDimension(filter, 0));
  }
```