Does TensorFloat-32 support `tf.compat.v1.nn.depthwise_conv2d_native`? How can I identify whether an op is supported by TensorFloat-32?
### Issue type

Performance

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

Yes

### Source

binary

### TensorFlow version

2.14.0-rc1

### 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?

I'm trying to use TensorFloat-32 to accelerate my model.
But I found that `tf.compat.v1.nn.depthwise_conv2d_native` is not currently supported by TensorFloat-32, by comparing the output with and without TensorFloat-32.

The [documentation](https://www.tensorflow.org/api_docs/python/tf/config/experimental/enable_tensor_float_32_execution) says _"Note TensorFloat-32 is not always used in supported ops, as only inputs of certain shapes are supported. Support for more input shapes and more ops may be added in the future."_
I also found that #46168 and #58047 mention some ops that are not supported by TensorFloat-32.
So I'm wondering if there is a way to identify whether an op is supported by TensorFloat-32?


### Standalone code to reproduce the issue

```python
import numpy as np
import tensorflow as tf
print(tf.version.GIT_VERSION, tf.version.VERSION, flush=True)
print(tf.config.list_physical_devices(), flush=True)


x_in = np.random.rand(1,7,7,16)
kernel_in = np.random.rand(3,3,16,16)

tf.config.experimental.enable_tensor_float_32_execution(True)
x = tf.constant(x_in, dtype=tf.float32)
kernel = tf.constant(kernel_in, dtype=tf.float32)
res_tf32 = tf.compat.v1.nn.depthwise_conv2d_native(x, kernel, strides=[1, 1, 1, 1], padding='VALID')

tf.config.experimental.enable_tensor_float_32_execution(False)
x = tf.constant(x_in, dtype=tf.float32)
kernel = tf.constant(kernel_in, dtype=tf.float32)
res_full = tf.compat.v1.nn.depthwise_conv2d_native(x, kernel, strides=[1, 1, 1, 1], padding='VALID')

print("res_tf32==res_full:", np.all(res_tf32==res_full))
```


### Relevant log output

```text
v2.14.0-rc0-34-gdd01672d9a9 2.14.0-rc1
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
res_tf32==res_full: True
```
