**Reshaping functions for grids**

The user's request is to unify the function types. Rather than having different types for each function, they want all functions to accept and return a list of grids. I need to reshape the functions into a format where each function transforms grids as inputs and outputs. A possible approach is to write a module where all operations are unary functions that map a list of grids to another list of grids. This will allow operations to be applied sequentially in a pipeline with uniform input and output types.

**Proposal for function composition**

It seems like the user wants a unified way of handling grid operations, where all functions take lists of grids as inputs and return lists of grids as outputs. My idea is to create a "wrapper" that would convert original functions on a single grid into functions that can handle many grids at once. For binary operations, I'd map the grid pairs accordingly. This would allow the functions to stay composable, which seems to be their goal. Essentially, I’ll build a system to lift operations up to handle lists instead of individual grids directly.

Thought for 7 seconds

Below is a small framework you can drop into a new module (say, `dsl_unified.py`) that automatically turns _any_ of your existing DSL functions into a unified, composable form:

```python
# dsl_unified.py

from typing import List
import inspect
import dsl as _dsl    # your original file

Grid = tuple  # shorthand; replace with your actual Grid alias

def lift(fn):
    """
    Turn an N-ary function fn: Grid,Grid… → Grid
    into a unary function F: List[Grid] → List[Grid],
    by pulling its first N grids out of the list and
    returning its single result wrapped in a list.
    """
    arity = inspect.signature(fn).parameters.__len__()

    def F(grids: List[Grid]) -> List[Grid]:
        if len(grids) < arity:
            raise ValueError(f"{fn.__name__} needs {arity} grids, got {len(grids)}")
        # call fn on the first arity grids
        result = fn(*grids[:arity])
        # wrap the single-grid output in a list
        return [result]

    F.__name__ = fn.__name__
    return F

# Now lift *all* of your grid-oriented DSL functions:
# (you can pick and choose; here’s a representative subset)

identity   = lift(_dsl.identity)
crop       = lift(_dsl.crop)
canvas     = lift(_dsl.canvas)
rot90      = lift(_dsl.rot90)
rot180     = lift(_dsl.rot180)
fill       = lift(_dsl.fill)
paint      = lift(_dsl.paint)
normalize  = lift(_dsl.normalize)
objects    = lift(_dsl.objects)
hmirror    = lift(_dsl.hmirror)
vmirror    = lift(_dsl.vmirror)
upscale    = lift(_dsl.upscale)
downscale  = lift(_dsl.downscale)
hconcat    = lift(_dsl.hconcat)
vconcat    = lift(_dsl.vconcat)
# …and so on for every grid→grid or (grid,grid)→grid fn in dsl.py

# Example pipeline usage:

# Suppose you start with two grids, g1 and g2:
#   >>> gs = [g1, g2]

# 1) Normalize g1:
#   >>> gs = normalize(gs)
#   # now gs == [ normalize(g1) ]

# 2) Upscale that result by factor 2:
#   >>> gs = upscale(gs)        # but upscale originally took two args:
#                                 # (element, factor) → element
#   ValueError: upscale needs 2 grids, got 1

# To handle numeric parameters you’d bake them into small one-off wrappers:

def upscale_by(factor: int):
    def _up(gs: List[Grid]) -> List[Grid]:
        return [ _dsl.upscale(gs[0], factor) ]
    return _up

# then use
#   >>> upscale2 = upscale_by(2)
#   >>> gs = upscale2(gs)

# 3) Finally paint onto g2 (the original second grid):
#   >>> gs = paint([ gs[0], g2 ])
#   # now gs == [ paint(gs[0], g2) ]

# You can now compose everything as a flat chain of unary ops:
#   pipeline = lambda gs: paint( upscale_by(2)( normalize(gs) ) + [ g2 ] )
```

### What’s going on?

- **`lift`** inspects the original function’s arity, then produces a new
  `F: List[Grid] → List[Grid]` which always

  1. pulls its required grids out of the front of the list
  2. calls the original
  3. wraps the single-grid result in a list again.

- Now **every** operation is a unary transformation on `List[Grid]`, so you can write pipelines of the form

  ```python
  result = f3( f2( f1(initial_list_of_grids) ) )
  ```

- For any function that originally required extra non-grid params (numbers, functions, etc.),
  you _bake_ those into tiny closures (like `upscale_by(2)`) that also expose the same `List[Grid] → List[Grid]` interface.

This way all your DSL primitives share exactly one signature and compose cleanly.
