Invalid `Conv2d` can be executed without compilation
Any update for this issue? @YangChenyuan Sorry for the late response!
I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/5f2c13f3189b6dc82794d58c01d9b62e/61879.ipynb).
@SuryanarayanaY Could you please have a look at this issue?
Thank you! @sushreebarsa Thanks for your further exploration and confirmation! Hi @YangChenyuan ,

This also seems issue with `tf.function` rather than XLA as per exercise in attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/f92d1a1d9a20c08139c5c716771e453e/61879_r1.ipynb). Without `tf.function` it works fine. Needs to dig more to find out the root cause.

Thank you!
### Issue type

Bug

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

Yes

### Source

source

### TensorFlow version

2.15.0-dev20230914

### 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 behavior?

An invalid `Conv2d` can be executed without compilation. By contrast, after using `@tf.function(jit_compile=True)`, it will raise error `Negative dimension size caused by subtracting 2 from 1 for '{{node conv2d_6/Conv2D}} ...`

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

"""
Don't use @tf.function(jit_compile=True)
"""

class Model(tf.keras.Model):

  def __init__(self):
    super(Model, self).__init__()
    self.conv2d = tf.keras.layers.Conv2D(5, 2, activation=tf.nn.relu)

  def call(self, x):
    conv2d = self.conv2d(x)
    return conv2d

# Initializing the model
m = Model()

# Inputs to the model
# This input results in a Conv2D kernel that has the shape (1, 2, 3, 5).
x1 = tf.constant([[[[1., 2., 3.], [4., 5., 6.]]]], shape=[1, 1, 2, 3])

y = m(x1)
print(y.shape)
# (1, 0, 1, 5)

"""
Using @tf.function(jit_compile=True)
"""
class Model(tf.keras.Model):

  def __init__(self):
    super(Model, self).__init__()
    self.conv2d = tf.keras.layers.Conv2D(5, 2, activation=tf.nn.relu)
  @tf.function(jit_compile=True)
  def call(self, x):
    conv2d = self.conv2d(x)
    return conv2d
# Initializing the model
m = Model()

# Inputs to the model
# This input results in a Conv2D kernel that has the shape (1, 2, 3, 5).
x1 = tf.constant([[[[1., 2., 3.], [4., 5., 6.]]]], shape=[1, 1, 2, 3])

y = m(x1)
"""

    ValueError: Exception encountered when calling layer 'conv2d_6' (type Conv2D).
    
    Negative dimension size caused by subtracting 2 from 1 for '{{node conv2d_6/Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], explicit_paddings=[], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](x, conv2d_6/Conv2D/ReadVariableOp)' with input shapes: [1,1,2,3], [2,2,3,5].
    
    Call arguments received by layer 'conv2d_6' (type Conv2D):
      • inputs=tf.Tensor(shape=(1, 1, 2, 3), dtype=float32)


Call arguments received by layer 'model_10' (type Model):
  • x=tf.Tensor(shape=(1, 1, 2, 3), dtype=float32)
"""
```


### Relevant log output

_No response_