Wrong answer from tflite model with certain configurations of depthwise conv2d
I am able to reproduce on nightly: [gist](https://colab.sandbox.google.com/gist/pkgoogle/11b39c4c2962fdcedce78dac2be1a1fa/61815.ipynb), The problem is the output tensor (in the TFLite case) is all zeroes.

Hi @majiddadashi, can you please take a look? Thanks.
### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Debian 11
- TensorFlow installation (pip package or built from source): pip package
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.13.0

### 2. Code

```
import tensorflow as tf
import numpy as np

tf.keras.utils.set_random_seed(0)

# Layer parameter values
imageSize = (8, 8)
numChannels = 2
fmt = 'channels_last'
kernelSize = (3, 1)
padding = 'same'
depthMult = 1
useBias = False
strides = (2, 2)

# Determine input shape
if fmt == 'channels_last':
    inShape = imageSize + (numChannels,)
else:
    inShape = (numChannels,) + imageSize

# Determine input size and create input data
inSize = (1,) + inShape
rng = np.random.default_rng(seed=999)
x = rng.random(size=inSize, dtype=np.float32)

# Construct Keras model
inp = tf.keras.layers.Input(shape=inShape, batch_size=1)
dconv = tf.keras.layers.DepthwiseConv2D(kernelSize, strides=strides, padding=padding, depth_multiplier=depthMult,
                                        data_format=fmt, activation=None, use_bias=useBias)(inp)
kModel = tf.keras.models.Model(inputs=inp, outputs=dconv)
kModel.compile()

# Run Keras model predict
kY = kModel.predict(x)
print(kY)

# Convert model to tflite
converter = tf.lite.TFLiteConverter.from_keras_model(kModel)
tfModel = converter.convert()

# Run tflite interpreter
interpreter = tf.lite.Interpreter(model_content=tfModel)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
interpreter.set_tensor(input_details[0]['index'], x)
interpreter.invoke()
tfY = interpreter.get_tensor(output_details[0]['index'])
print(tfY)

isequal = np.allclose(kY, tfY, rtol=1e-4, atol=1e-4)
print(isequal)
```

### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:

- Model produces wrong results - in this case all 0s

5. (optional) Any other info / logs

Some general observations on when this wrong answer occurs (all of the following must be met):
- One of the kernel_size dimensions is 1 (1xN or Nx1)
- Stride is not 1
- Padding is same

