dimension-specific `vmin` and `vmax` in `render_array`
A common use case for me is visualizing an _N_-d array where one axis represents varying semantics (like loss vs. accuracy), but it is still meaningful to visualize the joint array because of shared coordinates. In those cases, I found that the global arguments for `vmin` and `vmax` are limiting because the scales of the different semantic dimension are distinct.
It would be great to be able to specify a dimension-specific value for `vmin` and `vmax`.
Adapting an example from the docs:
```python
import numpy as np
import treescope
my_3d_array = np.einsum(
'ijk,i->ijk',
np.cos(np.arange(5*6*7).reshape((5,6,7)) * 0.1),
np.array([1.0, 2.0, 3.0, 4.0, 5.0]),
)
```
As a possible interface, I could think of adapting this global variant:
```python
treescope.render_array(my_3d_array, vmin=-5.0, vmax=5.0)
```
to a dimension-specific variant like:
```python
treescope.render_array(
my_3d_array,
axis_vmin={
0: (-1.0, -2.0, -3.0, -4.0, -5.0),
},
axis_vmax={
0: (1.0, 2.0, 3.0, 4.0, 5.0),
},
)
```
Is this in scope of `render_array`?
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