High Numerical Errors in Mish Activation with FLOAT16 Precision on Neural Engine

## 🐞Describing the bug
The built-in Mish activation function in `coremltools` introduces significant numerical errors in Core ML models when using 16-bit floating point precision (FLOAT16) on configurations with `ComputeUnit=CPU_AND_NE`. Specifically, converting models that utilize the Mish activation results in substantial discrepancies in output predictions compared to the original model, leading to high error rates across various metrics.

## Stack Trace
N/A

## To Reproduce
Follow the steps below to reproduce the high numerical errors using the built-in Mish activation function:

1. **Clone the KataGo repository:**
```bash
git clone --branch v1.15.3-coreml1 https://github.com/ChinChangYang/KataGo.git KataGo-v1.15.3-coreml1
cd KataGo-v1.15.3-coreml1/python
```

2. **Download a KataGo model in RAW checkpoint format:**
```bash
wget https://media.katagotraining.org/uploaded/networks/zips/kata1/kata1-b18c384nbt-s9996604416-d4316597426.zip
unzip kata1-b18c384nbt-s9996604416-d4316597426.zip
ln -s kata1-b18c384nbt-s9996604416-d4316597426/model.ckpt model.ckpt
```

3. **Install Python Modules:**
```bash
pip install torch coremltools matplotlib
```

4. **Evaluate the high error using the built-in Mish implementation:**
```bash
wget https://gist.githubusercontent.com/ChinChangYang/529ccdffb90b60d307550b067f2fbab8/raw/abc3050cfad77e1ec87c92f61bd4b8c1b4f6cc28/testcoremlerror_original.py
python testcoremlerror_original.py
```

**Expected Output:**
```
Mean Absolute Errors Across Samples:
var_2572:
FLOAT16: 1.042287
FLOAT32: 0.000095
linear_9:
FLOAT16: 3.587491
FLOAT32: 0.000245
linear_10:
FLOAT16: 2.812497
FLOAT32: 0.000182
linear_11:
FLOAT16: 2.498940
FLOAT32: 0.000269
var_2631:
FLOAT16: 0.079012
FLOAT32: 0.000011
```

5. **Evaluate the lower error using the alternative Mish implementation:**
```bash
wget https://gist.githubusercontent.com/ChinChangYang/b9d45f13a40ff738baa607a265a0b2c3/raw/8bf3ae8e66946451be7dbd0d6debdae9d8e82fcf/testcoremlerror_workaround.py
python testcoremlerror_workaround.py
```

**Expected Output:**
```
Mean Absolute Errors Across Samples:
var_2572:
FLOAT16: 0.008898
FLOAT32: 0.000395
linear_9:
FLOAT16: 0.018509
FLOAT32: 0.000812
linear_10:
FLOAT16: 0.014011
FLOAT32: 0.000628
linear_11:
FLOAT16: 0.016918
FLOAT32: 0.000859
var_2631:
FLOAT16: 0.001414
FLOAT32: 0.000036
```

## System environment (please complete the following information):
- coremltools version: 8.0
- OS: MacOS 15.0
- Any other relevant version information:
- PyTorch: 2.4.1

## Additional context

The issue arises specifically when using `ComputeUnit=CPU_AND_NE` with `Precision=FLOAT16`. The built-in Mish activation function in `coremltools` leads to high numerical errors, as evidenced by metrics such as `winrateError`, `leadError`, and others showing discrepancies upwards of 25%. Switching to an alternative Mish implementation drastically reduces these errors to below 1%, albeit with a 32% increase in inference time due to the additional operators introduced.

This problem is isolated to 16-bit floating point precision on the Neural Engine (NE), as experiments with other compute units and precision settings (e.g., FLOAT32) do not exhibit the same high error rates. The significant reduction in error using the alternative Mish implementation suggests that the built-in Mish operator may have implementation issues when used in this specific configuration.

*This issue was generated based on a detailed analysis of numerical errors in Core ML models using the Mish activation function with 16-bit precision, as documented in the [related blog post](https://chinchangyang.github.io/precision,/mish,/float16,/katago,/network,/coreml,/coremltools/2024/10/05/discovering-high-error-of-mish-activation-output.html). Further investigation and collaboration from the `coremltools` engineering team would be greatly appreciated to resolve this matter.*

Start by running testcoremlerror_original.py from the linked reproduction, then compare its FLOAT16 CPU_AND_NE results with testcoremlerror_workaround.py. Trace the built-in and alternative Mish implementations used by the conversion and isolate the Neural Engine precision behavior. Done means the Mish path no longer produces the reported large errors without relying on the workaround.

Written by the indexing model from the issue text.