ValueError: Exception encountered when calling layer "13" (type Lambda).
This at most belongs to keras, not TF 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/61958">Yes</a>
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### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

tf 2.13

### Custom code

Yes

### OS platform and distribution

5.15.90.1-microsoft-standard-WSL2

### Mobile device

_No response_

### Python version

3.8.17

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

12.2

### GPU model and memory

_No response_

### Current behavior?

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ValueError:   Exception encountered when calling layer "13" (type Lambda).          Dimensions must be equal, but are 204 and 206 for '{{node 13/Add}} =   AddV2[T=DT_FLOAT](Placeholder, Placeholder_1)' with input shapes:   [?,64,204,204], [?,64,206,206].          Call arguments received by layer "13" (type Lambda):       • inputs=['tf.Tensor(shape=(None,   64, 204, 204), dtype=float32)', 'tf.Tensor(shape=(None, 64, 206, 206),   dtype=float32)']       • mask=None       • training=None
--




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### Standalone code to reproduce the issue

```shell
just run the following code to reproduce:

from onnx2keras import onnx_to_keras
import keras
import onnx
import sys
# sys.path.append("/root/MR")
onnx_model = onnx.load('yolov3_darknet53.onnx')
onnx_inputs = onnx_model.graph.input
print("===========================")
print(onnx_inputs)
# onnx_model = onnx.load('vgg11.onnx')
k_model = onnx_to_keras(onnx_model, ['x'], name_policy='renumerate', verbose=True)
keras.models.save_model(k_model, 'ssd_resnet50fpn_torch.h5', overwrite=True, save_format="h5")
```
the onnx file can be downloaded at https://pan.xunlei.com/s/VNf1FtBh2v6mP_QXJWVailBmA1?pwd=g43e#
```


### Relevant log output

```shell
Traceback (most recent call last):
  File "o2k.py", line 11, in <module>
    k_model = onnx_to_keras(onnx_model, ['x'], name_policy='renumerate', verbose=True)
  File "/root/miniconda3/envs/onnx/lib/python3.8/site-packages/onnx2keras/converter.py", line 175, in onnx_to_keras
    AVAILABLE_CONVERTERS[node_type](
  File "/root/miniconda3/envs/onnx/lib/python3.8/site-packages/onnx2keras/elementwise_layers.py", line 83, in convert_elementwise_add
    layers[node_name] = lambda_layer([input_0, input_1])
  File "/root/miniconda3/envs/onnx/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/root/miniconda3/envs/onnx/lib/python3.8/site-packages/onnx2keras/elementwise_layers.py", line 76, in target_layer
    layer = tf.add(
ValueError: Exception encountered when calling layer "LAYER_12" (type Lambda).

Dimensions must be equal, but are 204 and 206 for '{{node LAYER_12/Add}} = AddV2[T=DT_FLOAT](Placeholder, Placeholder_1)' with input shapes: [?,64,204,204], [?,64,206,206].

Call arguments received by layer "LAYER_12" (type Lambda):
  • inputs=['tf.Tensor(shape=(None, 64, 204, 204), dtype=float32)', 'tf.Tensor(shape=(None, 64, 206, 206), dtype=float32)']
  • mask=None
  • training=None
```
