Add support for truncation argument when calling a Pipeline
# 🚀 Feature request

Currently, only the `padding` argument [is supported](https://github.com/huggingface/transformers/blob/a39dfe4fb122c11be98a563fb8ca43b322e01036/src/transformers/pipelines.py#L500) when calling a pipeline, and it's not possible to pass `truncation` argument. For example, running the following code sample would raise an error:

```python
import transformers as trf

model = trf.pipeline(task='feature-extraction', model='bert-base-cased')
output = model('a sample text', padding=False, truncation=True)
```

## Motivation

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     related to a problem? e.g., I'm always frustrated when [...]. If this is related
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If toggling padding is supported, then why truncation shouldn't be?

## Your contribution

I think to achieve this, same as `padding`, only a `truncation` argument should be added to `_parse_and_tokenize` method and also when calling the tokenizer. If that's the case, I would be willing to work on a PR.
is there any workaround for this?  i'm seeing the `Token indices sequence length is longer than the specified maximum sequence length for this model (... > 512). Running this sequence through the model will result in indexing errors` when using TextClassificationPipeline.

(This is preventing me from upgrading to 3.x.)<|||||>routing this to @mfuntowicz @LysandreJik <|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>+1 on this<|||||>Hi, even though this has been closed as stale, without comment or supposed fix, it seems that in recent versions you can in fact pass both `truncation` and `padding` arguments to the pipeline's `__call__` method, and it will correctly use them when tokenizing. I've tested it with long texts that fail without the truncation argument, and it seems to work as expected. 