Add torchcodec in docstrings/tests for datasets 4.0 (#39156)

* fix dataset run_object_detection

* bump version

* keep same dataset actually

* torchcodec in docstrings and testing utils

* torchcodec in dockerfiles and requirements

* remove duplicate

* add torchocodec to all the remaining docker files

* fix tests

* support torchcodec in audio classification and ASR

* [commit to revert] build ci-dev images

* [commit to revert] trigger circleci

* [commit to revert] build ci-dev images

* fix

* fix modeling_hubert

* backward compatible run_object_detection

* revert ci trigger commits

* fix mono conversion and support torch tensor as input

* revert map_to_array docs + fix it

* revert mono

* nit in docstring

* style

* fix modular

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/clap/test_feature_extraction_clap.py

@@ -165,7 +165,7 @@ class ClapFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.Tes
     def _load_datasamples(self, num_samples):
         ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         # automatic decoding with librispeech
-        speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
+        speech_samples = ds.sort("id")[:num_samples]["audio"]
 
         return [x["array"] for x in speech_samples]