Update dl1 to dl2 script (Less required memory and less required step)

[SeiyaNozaki]

This PR updates the dl1 to dl2 step:

• Load each random forest model just before using it. Now the script loads all models at the beginning of the script and it consumes much memory. Just pass the model path to apply_models. This option was already implemented in apply_models, but not used for the script before.
• This script applies the particle classification models to the data twice for gammaness and reco_type. But reco_type can be set based on gammaness as suggested by @maxnoe here. Even, we can remove reco_type which will not be used for the analysis. https://github.com/cta-observatory/cta-lstchain/pull/1077#discussion_r1104715136

[codecov[bot]]

Codecov Report

Attention: Patch coverage is 93.75000% with 1 lines in your changes are missing coverage. Please review.

Project coverage is 72.87%. Comparing base (1713092) to head (24ecab8). Report is 65 commits behind head on main.

Files	Patch %	Lines
lstchain/scripts/lstchain_dl1_to_dl2.py	88.88%	1 Missing :warning:

Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #1224 +/- ## ========================================== + Coverage 72.67% 72.87% +0.19% ========================================== Files 132 133 +1 Lines 13655 13825 +170 ========================================== + Hits 9924 10075 +151 - Misses 3731 3750 +19 ```

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[SeiyaNozaki]

Concerning the first point, it was on purpose for multiple input files... https://github.com/cta-observatory/cta-lstchain/pull/985

So I will modify codes like

• If there is a single input file, each model will be loaded just before it is used
• If there are multiple input files, all modes will be loaded once at the beginning and there will be no reload of models for each file

[SeiyaNozaki]

I realized I didn't read carefully comments from @gabemery during the school... sorry!