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< from datasets import load_dataset
< 
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> from datasets import load_dataset
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<     Trainer,
<     TrainingArguments,
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> from optimum.graphcore import IPUConfig, IPUTrainer
> from optimum.graphcore import IPUTrainingArguments as TrainingArguments
> from optimum.graphcore.utils import check_min_version as gc_check_min_version
> 
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> # Will error if the minimal version of Optimum Graphcore is not installed. Remove at your own risks.
> gc_check_min_version("0.6.0.dev0")
> 
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<         default=False,
---
>         default=True,
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<     # Log on each process the small summary:
<     logger.warning(
<         f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
<         + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
<     )
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>     ipu_config = IPUConfig.from_pretrained(
>         training_args.ipu_config_name if training_args.ipu_config_name else model_args.model_name_or_path,
>         cache_dir=model_args.cache_dir,
>         revision=model_args.model_revision,
>         use_auth_token=True if model_args.use_auth_token else None,
>     )
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<         else DataCollatorForMultipleChoice(tokenizer=tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None)
---
>         else DataCollatorForMultipleChoice(tokenizer=tokenizer, pad_to_multiple_of=None)
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>     if not data_args.pad_to_max_length:
>         logging.warning(
>             "Not padding to max length might lead to batches with difference sequence lengths, which might not work as"
>             "expected on IPUs"
>         )
> 
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<     trainer = Trainer(
---
>     trainer = IPUTrainer(
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>         ipu_config=ipu_config,