Fix CI: ignore C901 ("some_func" is to complex) in `ruff`
_The documentation is not available anymore as the PR was closed or merged._ <details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006529 / 0.011353 (-0.004824) | 0.004527 / 0.011008 (-0.006481) | 0.098051 / 0.038508 (0.059543) | 0.028058 / 0.023109 (0.004949) | 0.368543 / 0.275898 (0.092645) | 0.397126 / 0.323480 (0.073646) | 0.005072 / 0.007986 (-0.002913) | 0.003377 / 0.004328 (-0.000952) | 0.076867 / 0.004250 (0.072617) | 0.040121 / 0.037052 (0.003069) | 0.373422 / 0.258489 (0.114933) | 0.403969 / 0.293841 (0.110128) | 0.031485 / 0.128546 (-0.097061) | 0.011673 / 0.075646 (-0.063973) | 0.321837 / 0.419271 (-0.097434) | 0.042828 / 0.043533 (-0.000704) | 0.370391 / 0.255139 (0.115252) | 0.391737 / 0.283200 (0.108538) | 0.084764 / 0.141683 (-0.056919) | 1.463114 / 1.452155 (0.010959) | 1.527042 / 1.492716 (0.034325) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.200964 / 0.018006 (0.182958) | 0.403967 / 0.000490 (0.403477) | 0.002439 / 0.000200 (0.002239) | 0.000070 / 0.000054 (0.000016) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023531 / 0.037411 (-0.013880) | 0.097424 / 0.014526 (0.082899) | 0.104854 / 0.176557 (-0.071703) | 0.165682 / 0.737135 (-0.571453) | 0.109416 / 0.296338 (-0.186922) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.431041 / 0.215209 (0.215832) | 4.326039 / 2.077655 (2.248384) | 2.085123 / 1.504120 (0.581003) | 1.922720 / 1.541195 (0.381525) | 2.006608 / 1.468490 (0.538118) | 0.703348 / 4.584777 (-3.881428) | 3.441516 / 3.745712 (-0.304196) | 1.875244 / 5.269862 (-3.394618) | 1.181341 / 4.565676 (-3.384336) | 0.083442 / 0.424275 (-0.340833) | 0.012966 / 0.007607 (0.005359) | 0.536047 / 0.226044 (0.310002) | 5.354856 / 2.268929 (3.085927) | 2.451064 / 55.444624 (-52.993560) | 2.076110 / 6.876477 (-4.800367) | 2.196507 / 2.142072 (0.054435) | 0.811196 / 4.805227 (-3.994032) | 0.152547 / 6.500664 (-6.348118) | 0.067978 / 0.075469 (-0.007491) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.196169 / 1.841788 (-0.645618) | 13.697234 / 8.074308 (5.622926) | 13.966652 / 10.191392 (3.775260) | 0.143735 / 0.680424 (-0.536688) | 0.016484 / 0.534201 (-0.517717) | 0.382349 / 0.579283 (-0.196934) | 0.401507 / 0.434364 (-0.032857) | 0.447297 / 0.540337 (-0.093041) | 0.529779 / 1.386936 (-0.857157) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006698 / 0.011353 (-0.004655) | 0.004608 / 0.011008 (-0.006400) | 0.076220 / 0.038508 (0.037712) | 0.027340 / 0.023109 (0.004231) | 0.344095 / 0.275898 (0.068197) | 0.374715 / 0.323480 (0.051235) | 0.004883 / 0.007986 (-0.003102) | 0.004658 / 0.004328 (0.000330) | 0.075381 / 0.004250 (0.071130) | 0.036099 / 0.037052 (-0.000953) | 0.340382 / 0.258489 (0.081893) | 0.383488 / 0.293841 (0.089647) | 0.031534 / 0.128546 (-0.097012) | 0.011735 / 0.075646 (-0.063912) | 0.085895 / 0.419271 (-0.333377) | 0.042226 / 0.043533 (-0.001306) | 0.340301 / 0.255139 (0.085162) | 0.366079 / 0.283200 (0.082879) | 0.088828 / 0.141683 (-0.052854) | 1.487880 / 1.452155 (0.035725) | 1.561318 / 1.492716 (0.068601) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.226366 / 0.018006 (0.208360) | 0.408934 / 0.000490 (0.408444) | 0.000396 / 0.000200 (0.000196) | 0.000060 / 0.000054 (0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024521 / 0.037411 (-0.012891) | 0.100167 / 0.014526 (0.085641) | 0.106480 / 0.176557 (-0.070077) | 0.156377 / 0.737135 (-0.580758) | 0.111709 / 0.296338 (-0.184630) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.436138 / 0.215209 (0.220928) | 4.370919 / 2.077655 (2.293265) | 2.066402 / 1.504120 (0.562282) | 1.862157 / 1.541195 (0.320962) | 1.920701 / 1.468490 (0.452211) | 0.695517 / 4.584777 (-3.889260) | 3.435558 / 3.745712 (-0.310154) | 1.864000 / 5.269862 (-3.405861) | 1.164134 / 4.565676 (-3.401543) | 0.083006 / 0.424275 (-0.341269) | 0.012751 / 0.007607 (0.005144) | 0.535405 / 0.226044 (0.309360) | 5.368530 / 2.268929 (3.099602) | 2.494197 / 55.444624 (-52.950427) | 2.161370 / 6.876477 (-4.715107) | 2.180345 / 2.142072 (0.038272) | 0.808076 / 4.805227 (-3.997151) | 0.151891 / 6.500664 (-6.348773) | 0.067643 / 0.075469 (-0.007826) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.334245 / 1.841788 (-0.507543) | 14.112805 / 8.074308 (6.038497) | 14.152303 / 10.191392 (3.960911) | 0.153492 / 0.680424 (-0.526932) | 0.016542 / 0.534201 (-0.517659) | 0.376013 / 0.579283 (-0.203270) | 0.386528 / 0.434364 (-0.047836) | 0.436461 / 0.540337 (-0.103876) | 0.519278 / 1.386936 (-0.867658) |

</details>
</details>

![](https://cml.dev/watermark.png#ce1d1076fc55ac49277398304e551f0b56c3c9e2 "CML watermark")

idk if I should have added this ignore to `ruff` too, but I added :) 