Remove deprecated PyTensor function functionality and reduce overhead

[ricardoV94]

Removing all the deprecated stuff that nobody uses

I think after this there's not much more fat to trim. We could try to avoid the input/output list thing with linkers that don't need it but we still need to sort them/ recognize kwargs which is pretty much what's left.

The C part of the call takes 100ns, so the "overhead" in this case is 200-250ns.
For reference, calling numpy.zeros(100) takes like ~250ns, and a python identity function is ~20ns.

Results of the new benchmark

Before

---------------------------------------------------------------------------------------------------------- benchmark: 4 tests ----------------------------------------------------------------------------------------------------------
Name (time in ns)                                          Min                    Max                  Mean              StdDev                Median                IQR            Outliers  OPS (Kops/s)            Rounds  Iterations
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_overhead_benchmar[NO-GC-trust_input=True]        618.2859 (1.0)      33,186.5717 (3.01)       698.3395 (1.0)      204.4863 (1.0)        675.5714 (1.0)      20.1427 (1.0)    4439;14591    1,431.9682 (1.0)      193799           7
test_overhead_benchmar[GC-trust_input=True]           690.9995 (1.12)     11,041.0001 (1.0)        804.4022 (1.15)     437.7705 (2.14)       770.9987 (1.14)     30.9992 (1.54)     264;1235    1,243.1592 (0.87)      35559           1
test_overhead_benchmar[NO-GC-trust_input=False]     1,622.9988 (2.62)     40,334.9986 (3.65)     1,784.9450 (2.56)     338.6002 (1.66)     1,772.9999 (2.62)     60.0012 (2.98)      167;349      560.2413 (0.39)      23213           1
test_overhead_benchmar[GC-trust_input=False]        1,632.9996 (2.64)     22,442.0000 (2.03)     1,784.2403 (2.55)     256.1013 (1.25)     1,763.0009 (2.61)     59.0007 (2.93)     980;1555      560.4626 (0.39)      61920           1
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

After

------------------------------------------------------------------------------------------------------------ benchmark: 4 tests -----------------------------------------------------------------------------------------------------------
Name (time in ns)                                          Min                     Max                  Mean                StdDev                Median                IQR            Outliers  OPS (Kops/s)            Rounds  Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_overhead_benchmar[NO-GC-trust_input=True]        317.7142 (1.0)       23,333.0000 (2.65)       358.4749 (1.0)        141.9661 (1.20)       348.5000 (1.0)      11.5000 (1.0)     2884;8322    2,789.5957 (1.0)      196079          14
test_overhead_benchmar[GC-trust_input=True]           440.9994 (1.39)       8,815.9995 (1.0)        492.2538 (1.37)       118.0708 (1.0)        490.9998 (1.41)     30.0006 (2.61)       35;252    2,031.4722 (0.73)      26596           1
test_overhead_benchmar[NO-GC-trust_input=False]     1,393.0003 (4.38)      13,054.9997 (1.48)     1,569.0200 (4.38)       274.1532 (2.32)     1,541.9992 (4.42)     59.0007 (5.13)      712;985      637.3405 (0.23)      26839           1
test_overhead_benchmar[GC-trust_input=False]        1,473.0012 (4.64)     135,623.9991 (15.38)    1,680.1193 (4.69)     1,107.7806 (9.38)     1,612.9998 (4.63)     50.9990 (4.43)     182;1043      595.1958 (0.21)      30192           1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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I also removed the strict zip, because passing strict=False is more than 100ns slower than not specifying it at all. Check out with this snippet:

a = tuple(range(10))
b = tuple(range(9))
%timeit tuple(zip(a, b, strict=False))
%timeit tuple(zip(a, b))
587 ns ± 8.29 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
458 ns ± 17.4 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

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📚 Documentation preview 📚: https://pytensor--1351.org.readthedocs.build/en/1351/