Numerical precision issue of operators selu, leakyRelu, softplus and their corresponding backward operators on Bfloat16 vs float32
Hi @wzzll123 ,

I have tested the code with tf-nightly and replicated the precisional differences wrt `float32` and `bfloat16`. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/65f24bed8192f64bdcf76bc81c923a93/67440.ipynb#scrollTo=KZg13iGhYGPP) for reference. There seems to be an issue. Hi @SuryanarayanaY , thanks for replying.

The precision discrepancies may stem from the fact that activation operators' computations don't incorporate precision improvement.  Similar [issues](https://github.com/pytorch/pytorch/issues/77942) have arisen in PyTorch, which were fixed by using precision improvement. Perhaps TensorFlow could address this issue similarly?
### Issue type

Bug

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

Yes

### Source

binary

### TensorFlow version

tf 2.16.1

### Custom code

Yes

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

3.10.9

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I'd like to bring to attention an issue concerning the numerical precision of several operators (selu, leaky, relu) when operating on Bfloat16 versus float32 data types. I conducted comparisons using 20,000 random tensors for these operators, assessing the outputs in both Bfloat16 and float32 and computing the discrepancies. My observations indicate that differences generated by TensorFlow are generally more pronounced compared to PyTorch. Particularly noteworthy is the significant error produced by the SeluGrad operator. The results are summarized in the table below:

| Operator           | TensorFlow | PyTorch |
|--------------------|------------|---------|
| selu               | 0.24918    | 0.12243 |
| leakyrelu          | 0.01875    | 0.00094 |
| softplus           | 0.05488    | 0.01554 |
| seluGrad      | 10.41794   | 0.12406 |
| leakyreluGrad | 0.01875    | 0.00094 |
| softplusGrad | 0.13502    | 0.12484 |

In a standalone code to reproduce the issue, I provide illustrative instances for seluGrad operators, where the output discrepancy between Bfloat16 and float32 can be as high as 10.4.

### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import numpy as np

features = tf.convert_to_tensor(np.array([-0.00112915]), dtype=tf.float32)
gradients = tf.convert_to_tensor(np.array([-14.6875]), dtype=tf.float32)

x = tf.Variable(features)
k = tf.constant(gradients)
with tf.GradientTape(persistent=True) as tape:
    y = tf.nn.selu(features=x)
    z = k*y
    fianl = tf.reduce_mean(z)
    
print('float32 gradient:',tape.gradient(z, x))


features = tf.cast(features, dtype=tf.bfloat16)
gradients = tf.cast(gradients, dtype=tf.bfloat16)
x = tf.Variable(features)
k = tf.constant(gradients)
with tf.GradientTape(persistent=True) as tape:
    y = tf.nn.selu(features=x)
    z = k*y
    fianl = tf.reduce_mean(z)
print('float16 gradient:',tape.gradient(z, x))
```


### Relevant log output

```shell
float32 gradient: tf.Tensor([-25.792944], shape=(1,), dtype=float32)
bfloat16 gradient: tf.Tensor([-15.375], shape=(1,), dtype=bfloat16)
```
