add a feature to drop checkpoint layers
This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days. Looks good to me.  Approving MR as review from Marcin is complete.
# What does this PR do ?

1. Adds a feature similar to mcore's --skip-train flag.
3. Adds drop-layers feature support.
4. Adds script which loads the ckpt, trims needed layers, and saves trimmed checkpoint.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* Trim the model. Please, note that layer indices start from 1. Llama3 8b model example:
```ubuntu
python -m torch.distributed.launch --nproc_per_node=8 \
     NeMo/examples/nlp/language_modeling/megatron_gpt_drop_layers.py \
     --path_to_nemo /path/to/model.nemo \
     --path_to_save /path/to/save/trimmed_model.nemo \
     --tensor_model_parallel_size 8 \
     --pipeline_model_parallel_size 1 \
     --gpus_per_node 8 \
     --drop_layers 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
```
If you want to save trimmed model in zarr ckpt format (deprecated), add this flag to the command:
```ubuntu
--zarr
```

* Validate the model:
```ubuntu
python NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
  --config-path=/path/to/folder/with/model/config \
  --config-name=llama3_8b.yaml \
  trainer.limit_val_batches=number_of_batches_in_validation_step \
  model.restore_from_path=/path/to/trimmed_model.nemo \
  model.skip_train=True \
  model.data.data_impl=mock \
  model.data.data_prefix=[]
```
To use specific dataset instead of mock dataset:
```ubuntu
model.data.data_impl=mmap \
model.data.data_prefix=["path/to/datafile1", "path/to/datafile2"]
```

* Validate Original Model

To validate the original model without specific layers, use the following script:

```ubuntu
  python /NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py \
    --config-path=/path/to/folder/with/model/config \
    --config-name=llama3_8b.yaml \
    trainer.limit_val_batches=<limit_val_batches> \
    model.restore_from_path=/path/to/original_model.nemo \
    model.skip_train=True \
    model.data.data_impl=mock \
    model.data.data_prefix=[] \
    model.drop_layers=[16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]
```

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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

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# Additional Information
* Related to # (issue)
