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unit: def load_checkpoint()
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unit: def __init__()
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unit: def train_dataloader()
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unit: def train_dataloader()
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unit: def listdir()
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unit: def __init__()
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unit: def __init__()
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unit: def __init__()
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unit: def full_model_state_dict()
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unit: def dump()
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id: 327
unit: def load()
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id: 328
unit: def __repr__()
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id: 329
unit: def __str__()
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size: 2 LOC
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id: 330
unit: def save_top_k()
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size: 2 LOC
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number of parameters: 1

id: 331
unit: def state_dict()
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id: 332
unit: def get_logger()
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size: 2 LOC
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id: 333
unit: def is_slurm_run()
file: src/hyperpod_nemo_adapter/utils/general_utils.py
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size: 2 LOC
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id: 334
unit: def is_action_rank()
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number of parameters: 1

id: 335
unit: def get_coordinator_rank()
file: src/hyperpod_nemo_adapter/utils/get_rank.py
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size: 2 LOC
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number of parameters: 1

id: 336
unit: def current_replication_group()
file: src/hyperpod_nemo_adapter/utils/app_state.py
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size: 2 LOC
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id: 337
unit: def current_replication_group()
file: src/hyperpod_nemo_adapter/utils/app_state.py
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id: 338
unit: def replication_coordinator_rank()
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id: 339
unit: def replication_coordinator_rank()
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id: 340
unit: def fsdp_process_group()
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size: 2 LOC
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id: 341
unit: def fsdp_process_group()
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size: 2 LOC
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id: 342
unit: def is_fsdp_action_rank()
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size: 2 LOC
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id: 343
unit: def is_fsdp_action_rank()
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size: 2 LOC
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id: 344
unit: def fsdp_coordinator_rank()
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size: 2 LOC
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id: 345
unit: def fsdp_coordinator_rank()
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size: 2 LOC
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id: 346
unit: def initialize_model_parallel_for_nemo()
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size: 2 LOC
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unit: def _set_random_seed()
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id: 348
unit: def setup_args_for_sm()
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size: 2 LOC
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id: 349
unit: def seed()
file: src/hyperpod_nemo_adapter/collections/data/base.py
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number of parameters: 1

id: 350
unit: def get_batch()
file: src/hyperpod_nemo_adapter/collections/data/base.py
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size: 2 LOC
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id: 351
unit: def get_val_batch()
file: src/hyperpod_nemo_adapter/collections/data/base.py
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size: 2 LOC
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number of parameters: 2

id: 352
unit: def train_dataloader()
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size: 2 LOC
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id: 353
unit: def val_dataloader()
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size: 2 LOC
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id: 354
unit: def test_dataloader()
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size: 2 LOC
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id: 355
unit: def resume_from_sequence_number()
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size: 2 LOC
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id: 356
unit: def resume_from_sequence_number()
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size: 2 LOC
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number of parameters: 2

id: 357
unit: def __init__()
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size: 2 LOC
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unit: def train_dataloader()
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size: 2 LOC
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unit: def val_dataloader()
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size: 2 LOC
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unit: def get_batch()
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size: 2 LOC
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number of parameters: 2

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unit: def get_val_batch()
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size: 2 LOC
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id: 362
unit: def get_batch()
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size: 2 LOC
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number of parameters: 2

id: 363
unit: def get_val_batch()
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size: 2 LOC
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unit: def __init__()
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size: 2 LOC
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id: 365
unit: def get_batch()
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size: 2 LOC
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number of parameters: 2

id: 366
unit: def val_dataloader()
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size: 2 LOC
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id: 367
unit: def get_batch()
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size: 2 LOC
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number of parameters: 2

id: 368
unit: def get_val_batch()
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size: 2 LOC
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number of parameters: 2

id: 369
unit: def val_dataloader()
file: src/hyperpod_nemo_adapter/collections/data/dummy_dpo_data_module.py
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size: 2 LOC
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id: 370
unit: def get_val_batch()
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size: 2 LOC
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id: 371
unit: def __init__()
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size: 2 LOC
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id: 372
unit: def get_val_batch()
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size: 2 LOC
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id: 373
unit: def dataset()
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size: 2 LOC
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number of parameters: 1

id: 374
unit: def __len__()
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size: 2 LOC
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id: 375
unit: def __getitem__()
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unit: def __len__()
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unit: def __len__()
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id: 378
unit: def __init__()
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id: 379
unit: def __len__()
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id: 380
unit: def reset_parameters()
file: src/hyperpod_nemo_adapter/collections/model/nlp/custom_models/modeling_deepseek.py
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size: 2 LOC
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id: 381
unit: def reset_parameters()
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size: 2 LOC
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id: 382
unit: def yarn_find_correction_dim()
file: src/hyperpod_nemo_adapter/collections/model/nlp/custom_models/modeling_deepseek.py
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size: 2 LOC
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id: 383
unit: def reset_parameters()
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unit: def _shape()
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size: 2 LOC
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id: 385
unit: def get_input_embeddings()
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size: 2 LOC
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id: 386
unit: def set_input_embeddings()
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size: 2 LOC
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id: 387
unit: def get_input_embeddings()
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id: 388
unit: def set_input_embeddings()
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id: 389
unit: def get_output_embeddings()
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size: 2 LOC
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id: 390
unit: def set_output_embeddings()
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size: 2 LOC
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unit: def set_decoder()
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size: 2 LOC
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unit: def get_decoder()
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id: 393
unit: def get_input_embeddings()
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size: 2 LOC
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id: 394
unit: def set_input_embeddings()
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size: 2 LOC
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id: 395
unit: def set_config_mapping_hf_to_recipe_aliases()
file: src/hyperpod_nemo_adapter/collections/model/sagemaker_base_model.py
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size: 2 LOC
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unit: def use_peft()
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number of parameters: 1

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unit: def peft_type()
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size: 2 LOC
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number of parameters: 1

id: 398
unit: def do_patch_attn_context_parallel()
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size: 2 LOC
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id: 399
unit: def forward()
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number of parameters: 3

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unit: def on_train_batch_start()
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size: 2 LOC
McCabe index: 1
number of parameters: 3

id: 401
unit: def list_available_models()
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size: 2 LOC
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number of parameters: 1

id: 402
unit: def setup_training_data()
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size: 2 LOC
McCabe index: 1
number of parameters: 1

id: 403
unit: def setup_validation_data()
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size: 2 LOC
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id: 404
unit: def pytorch_model()
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size: 2 LOC
McCabe index: 2
number of parameters: 1

id: 405
unit: def load_local_model_state_dict()
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size: 2 LOC
McCabe index: 1
number of parameters: 2

id: 406
unit: def remove_checkpoint()
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size: 2 LOC
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number of parameters: 3

id: 407
unit: def restore_checkpoint_after_setup()
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size: 2 LOC
McCabe index: 1
number of parameters: 1

id: 408
unit: def get_position_embedding_on_this_context_parallel_rank()
file: src/hyperpod_nemo_adapter/patches/patch_llama_flash_attn_cp.py
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size: 2 LOC
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