CSR-based sparse arrays?

@inducer @kaushikcfd As discussed earlier today, the motivation is to extend the syntax to support batching across a sparse array.

For example, given an expression for per-box map (I am using python syntax to represent expression IOs):
```python
def per_box_expr(box_data: Array[dtype, (ndim_in, _n_box_points)]
) -> Array[dtype, (ndim_out, _n_box_points)]:
...
return per_box_output
```
A "CSR batching" here means applying the per-box map to a list of boxes with inhomogeneous point counts, which does the equivalent of the following:
```python
def csr_apply(
expr: Array[dtype, (ndim_out, _n_box_points)],
data_lists: Array[dtype, (ndim_in, n_total_points)],
data_starts: Array[int, (n_boxes + 1, )],
axis: int=0) -> Array[dtype, (ndim_out, n_total_points)]:

# this specifies the computation to be carried out, not a valid implementation
output = Array(dtype, (ndim_out, n_total_points))
for ib in range(n_boxes):
start, stop = (data_starts[ib], data_starts[ib+1])
box_data = data_lists[:, start:stop]
output[:, start:stop] = per_box_expr(box_data)

return output
```
Note that `start, stop, _n_box_points` needs to be special placeholders in order to handle data-dependency.

@kaushikcfd also suggested a more general interface by supplying CSR arrays similar interface to normal arrays, and add the equivalence of `numpy.apply_along_axis` support for arrays.

The issue names no files, tests, or entry points. Start by locating the existing array syntax and batching implementation, then compare it with the CSR examples and the proposed numpy.apply_along_axis-style interface; the work is done when a concrete supported interface and its behavior for inhomogeneous point counts are defined and implemented.

Written by the indexing model from the issue text.