Replace one letter import in docs
_The documentation is not available anymore as the PR was closed or merged._ > Thanks for the docs fix for consistency.
> 
> Again for consistency, it would be nice to make the same fix across all the docs, e.g.
> 
> https://github.com/huggingface/datasets/blob/310cdddd1c43f9658de172b85b6509d07d5e31a1/docs/source/image_classification.mdx?plain=1#L41

Excellent point! @albertvillanova Should be all of them now :) <details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<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) | 0.008776 / 0.011353 (-0.002576) | 0.004534 / 0.011008 (-0.006474) | 0.101921 / 0.038508 (0.063413) | 0.029995 / 0.023109 (0.006886) | 0.307180 / 0.275898 (0.031282) | 0.371001 / 0.323480 (0.047521) | 0.007089 / 0.007986 (-0.000896) | 0.003474 / 0.004328 (-0.000855) | 0.079498 / 0.004250 (0.075248) | 0.036522 / 0.037052 (-0.000531) | 0.311729 / 0.258489 (0.053240) | 0.349861 / 0.293841 (0.056020) | 0.033815 / 0.128546 (-0.094731) | 0.011435 / 0.075646 (-0.064211) | 0.322924 / 0.419271 (-0.096347) | 0.040981 / 0.043533 (-0.002552) | 0.306174 / 0.255139 (0.051035) | 0.331979 / 0.283200 (0.048780) | 0.091293 / 0.141683 (-0.050389) | 1.480935 / 1.452155 (0.028780) | 1.522022 / 1.492716 (0.029306) |

### 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) | 0.195053 / 0.018006 (0.177047) | 0.424898 / 0.000490 (0.424408) | 0.003869 / 0.000200 (0.003669) | 0.000075 / 0.000054 (0.000021) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024323 / 0.037411 (-0.013088) | 0.098061 / 0.014526 (0.083535) | 0.105770 / 0.176557 (-0.070787) | 0.145799 / 0.737135 (-0.591336) | 0.109109 / 0.296338 (-0.187230) |

### 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) | 0.420434 / 0.215209 (0.205225) | 4.194781 / 2.077655 (2.117126) | 2.030498 / 1.504120 (0.526378) | 1.885314 / 1.541195 (0.344120) | 1.996485 / 1.468490 (0.527995) | 0.708540 / 4.584777 (-3.876237) | 3.400694 / 3.745712 (-0.345018) | 2.888704 / 5.269862 (-2.381157) | 1.578100 / 4.565676 (-2.987577) | 0.082150 / 0.424275 (-0.342125) | 0.012277 / 0.007607 (0.004669) | 0.527312 / 0.226044 (0.301268) | 5.289566 / 2.268929 (3.020637) | 2.369997 / 55.444624 (-53.074628) | 2.040365 / 6.876477 (-4.836112) | 2.298857 / 2.142072 (0.156785) | 0.808446 / 4.805227 (-3.996781) | 0.149355 / 6.500664 (-6.351309) | 0.065993 / 0.075469 (-0.009477) |

### 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) | 1.231829 / 1.841788 (-0.609959) | 13.874762 / 8.074308 (5.800454) | 13.464379 / 10.191392 (3.272987) | 0.151105 / 0.680424 (-0.529319) | 0.028689 / 0.534201 (-0.505512) | 0.398720 / 0.579283 (-0.180564) | 0.402108 / 0.434364 (-0.032256) | 0.463426 / 0.540337 (-0.076912) | 0.541919 / 1.386936 (-0.845017) |

</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) | 0.006979 / 0.011353 (-0.004373) | 0.004723 / 0.011008 (-0.006285) | 0.099172 / 0.038508 (0.060664) | 0.027970 / 0.023109 (0.004861) | 0.415096 / 0.275898 (0.139198) | 0.455916 / 0.323480 (0.132437) | 0.005950 / 0.007986 (-0.002036) | 0.003423 / 0.004328 (-0.000906) | 0.075512 / 0.004250 (0.071262) | 0.040894 / 0.037052 (0.003842) | 0.419810 / 0.258489 (0.161321) | 0.461913 / 0.293841 (0.168072) | 0.033014 / 0.128546 (-0.095532) | 0.011613 / 0.075646 (-0.064033) | 0.320983 / 0.419271 (-0.098289) | 0.049902 / 0.043533 (0.006369) | 0.426378 / 0.255139 (0.171239) | 0.445594 / 0.283200 (0.162394) | 0.098978 / 0.141683 (-0.042705) | 1.485724 / 1.452155 (0.033570) | 1.563978 / 1.492716 (0.071262) |

### 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) | 0.232137 / 0.018006 (0.214131) | 0.432785 / 0.000490 (0.432296) | 0.006173 / 0.000200 (0.005973) | 0.000085 / 0.000054 (0.000031) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024924 / 0.037411 (-0.012487) | 0.102878 / 0.014526 (0.088352) | 0.107976 / 0.176557 (-0.068581) | 0.143581 / 0.737135 (-0.593554) | 0.111644 / 0.296338 (-0.184694) |

### 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) | 0.490902 / 0.215209 (0.275693) | 4.914060 / 2.077655 (2.836405) | 2.569465 / 1.504120 (1.065345) | 2.346872 / 1.541195 (0.805677) | 2.412047 / 1.468490 (0.943557) | 0.704975 / 4.584777 (-3.879802) | 3.443669 / 3.745712 (-0.302043) | 3.172055 / 5.269862 (-2.097807) | 1.332152 / 4.565676 (-3.233525) | 0.083023 / 0.424275 (-0.341252) | 0.012699 / 0.007607 (0.005092) | 0.592511 / 0.226044 (0.366466) | 5.916376 / 2.268929 (3.647448) | 3.028472 / 55.444624 (-52.416152) | 2.691159 / 6.876477 (-4.185318) | 2.786132 / 2.142072 (0.644060) | 0.814045 / 4.805227 (-3.991182) | 0.156630 / 6.500664 (-6.344034) | 0.071330 / 0.075469 (-0.004139) |

### 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) | 1.277936 / 1.841788 (-0.563852) | 14.331367 / 8.074308 (6.257059) | 13.685694 / 10.191392 (3.494302) | 0.138915 / 0.680424 (-0.541509) | 0.016844 / 0.534201 (-0.517357) | 0.390307 / 0.579283 (-0.188976) | 0.385207 / 0.434364 (-0.049157) | 0.448128 / 0.540337 (-0.092210) | 0.532609 / 1.386936 (-0.854327) |

</details>
</details>

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

This PR updates a code example for consistency across the docs based on [feedback from this comment](https://github.com/huggingface/transformers/pull/20925/files/9fda31634d203a47d3212e4e8d43d3267faf9808#r1058769500):

"In terms of style we usually stay away from one-letter imports like this (even if the community uses them) as they are not always known by beginners and one letter is very undescriptive. Here it wouldn't change anything to use albumentations instead of A."   


