Truncated Normal initializer doesn't match PyTorch

### System information
- WSL 2 on Windows
- Flax 0.8.4, JAX 0.4.30
- Python 3.10
- RTX 2080
- CUDA 12.3

Both `nn.initializers.truncated_normal` and `jax.nn.initializers.truncated_normal` aren't similar enough to PyTorch's [`nn.init.trunc_normal_`](https://pytorch.org/docs/stable/nn.init.html#torch.nn.init.trunc_normal_). All of these use a lower of -2 and upper of 2 by default.

I'm running a test to make sure the outputs are similar if given the same arguments.

Here's my JAX code.
```python
import jax
import jax.numpy as jnp
from jax import random
from flax import linen as nn

def summary_stats(name, x):
print(f'Stats for {name}:')
print('shape: ', x.shape)
print('min: ', x.min())
print('max: ', x.max())
print('std: ', jnp.std(x))
# print(x)

def make_trunc(key, stddev):
lower = -2
upper = -lower
shape = (4096,)
return nn.initializers.truncated_normal(stddev, lower=lower, upper=upper)(key, shape=shape)
# return jax.nn.initializers.truncated_normal(stddev, lower=lower, upper=upper)(key, shape=shape)

summary_stats('trunc', make_trunc(random.key(0), .02))
summary_stats('trunc', make_trunc(random.key(1), .04))
summary_stats('trunc', make_trunc(random.key(2), .06))
```

Here's my PyTorch code:
```python
import torch
import torch.nn as nn

def summary_stats(name, x):
print(f'Stats for {name}:')
print('shape: ', x.shape)
print('min: ', x.min().item())
print('max: ', x.max().item())
print('std: ', x.std().item())
# print(x)

t = torch.zeros((4096,))

nn.init.trunc_normal_(t, std=0.02)
summary_stats('pytorch', t)
nn.init.trunc_normal_(t, std=0.04)
summary_stats('pytorch', t)
nn.init.trunc_normal_(t, std=0.06)
summary_stats('pytorch', t)
```

JAX output:
```
Stats for trunc:
shape: (4096,)
min: -0.039960504
max: 0.039985776
std: 0.018035976
Stats for trunc:
shape: (4096,)
min: -0.0797701
max: 0.07959695
std: 0.03491081
Stats for trunc:
shape: (4096,)
min: -0.11983735
max: 0.119733654
std: 0.053539284
```

PyTorch output:
```
Stats for pytorch:
shape: torch.Size([4096])
min: -0.06634494662284851
max: 0.0743524581193924
std: 0.020439231768250465
Stats for pytorch:
shape: torch.Size([4096])
min: -0.13382470607757568
max: 0.12441056221723557
std: 0.03931436687707901
Stats for pytorch:
shape: torch.Size([4096])
min: -0.22086666524410248
max: 0.20988918840885162
std: 0.05979840084910393
```

**Although the std values look close enough, the min and max seem off.**

However, let's look at the JAX output again if I set `lower=-4`, even though PyTorch is using -2.

JAX output:
```
Stats for trunc:
shape: (4096,)
min: -0.07253291
max: 0.076029524
std: 0.020624608
Stats for trunc:
shape: (4096,)
min: -0.13531744
max: 0.12941647
std: 0.039439432
Stats for trunc:
shape: (4096,)
min: -0.21360189
max: 0.20679429
std: 0.060959056
```

Now min/max line up with PyTorch better. I haven't figured out in the source code what explains this, but it would be nice to document it if it's an intended design.