How to convert KerasTensor to numpy array or TensorFlow EagerTensor?
@kiratben,
The Keras Tensor cannot be converted to Numpy array directly, You can please try to convert the  Keras tensor to Tensor and from the Tensor you can convert to the numpy.

Also please have a look at this comment from the form discussion and the SO [link](https://stackoverflow.com/questions/69067933/how-to-convert-keras-tensor-into-numpy-array) for the reference.
https://discuss.tensorflow.org/t/get-values-of-kerastensor/2423
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<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Others

### Have you reproduced the bug with TF nightly?

Yes

### Source

source

### Tensorflow Version

2.9.2

### 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 Behaviour?

I have a KerasTensor object with shape (None, 128, 128, 1) that I need to pass to an OpenCV function. However, I'm having trouble converting the KerasTensor to either a numpy array or a TensorFlow EagerTensor that can be accepted by the function. Specifically, I want to convert the KerasTensor to a format that is compatible with OpenCV's input requirements. Any suggestions on how to achieve this conversion?
I tried using tensor.numpy() to convert the KerasTensor to a numpy array, but I got an AttributeError: 'KerasTensor' object has no attribute 'numpy'.


### Standalone code to reproduce the issue

```python
import tensorflow as tf
from tensorflow import keras

# Define the shape of the input tensor
input_shape = (128, 128, 1)

# Create an input tensor with the specified shape
input_tensor = keras.layers.Input(shape=input_shape)

# Create a model that uses the input tensor
x = keras.layers.Conv2D(32, 3, activation='relu', padding='same')(input_tensor)
x = keras.layers.Conv2D(64, 3, activation='relu', padding='same')(x)
output_tensor = keras.layers.Conv2D(1, 3, activation='sigmoid', padding='same')(x)

print(type(output_tensor)) # <class 'keras.engine.keras_tensor.KerasTensor'>

# Convert the output tensor to a numpy array
print(type(output_tensor.numpy())) # AttributeError: 'KerasTensor' object has no attribute 'numpy'
print(type(tf.keras.backend.eval(output_tensor))) #AttributeError: 'KerasTensor' object has no attribute 'numpy'`
```


### Relevant log output

_No response_</details>