Relu Grad Shape Issues
@profhulk-MI,
Could you please provide the complete standalone code or the colab gist to reproduce the issue which helps us to debug the issue in an effective way. Thank you! This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
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<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Others

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

No

### Source

source

### Tensorflow Version

2.12.0

### Custom Code

Yes

### OS Platform and Distribution

colab

### 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?

Just trying to implement 3D CNN for CT images, and suddenly came across this error.
Couldn't get much on the internet.
Below is the snippet of the error.

grads_and_vars = self.compute_gradients(loss, var_list, tape)
    File "/usr/local/lib/python3.10/dist-packages/keras/optimizers/optimizer.py", line 275, in compute_gradients
      grads = tape.gradient(loss, var_list)
Node: 'gradient_tape/model_7/3dcnn/conv3d_24/ReluGrad'
Inputs to operation gradient_tape/model_7/3dcnn/conv3d_24/ReluGrad of type ReluGrad must have the same size and shape.  Input 0: [1,0,1875,256] != input 1: [1,0,125,15,256]
	 [[{{node gradient_tape/model_7/3dcnn/conv3d_24/ReluGrad}}]] [Op:__inference_train_function_52480]

### Standalone code to reproduce the issue

```shell
x = Conv3D(filters=64, kernel_size=(3,3,3),strides=1,activation="relu",dilation_rate=(1,1,1),kernel_regularizer=regularizers.L1L2(l1=1e-5, l2=1e-4),input_shape=(512, 512, 20, 3))(inputs)
    x = BatchNormalization()(x)
    x = MaxPool3D(pool_size=(2, 2, 1))(x)
    x = Conv3D(filters=128, kernel_size=(3,3,3),strides=1,dilation_rate=(1,1,1),kernel_regularizer=regularizers.L1L2(l1=1e-5, l2=1e-4),activation="relu")(x)
    x = BatchNormalization()(x)
    x = MaxPool3D(pool_size=(2, 2, 1))(x)
    x = Conv3D(filters=256, kernel_size=(2,2,2),strides=1,dilation_rate=(1,1,1), kernel_regularizer=regularizers.L1L2(l1=1e-5, l2=1e-4),activation="relu")(x)
    x = BatchNormalization()(x)
    x = Conv3D(filters=512, kernel_size=(2,2,2),strides=2,dilation_rate=(1,1,1), activation="relu")(x)
```


### Relevant log output

_No response_</details>