class_docstr: A training loop that uses the stochastic local closed world assumption training approach.
  
  [ruffinelli2020]_ call the sLCWA ``NegSamp`` in their work.
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docstr: Initialize the training loop.
  
  :param negative_sampler: The class, instance, or name of the negative sampler
  :param negative_sampler_kwargs: Keyword arguments to pass to the negative sampler class on instantiation
      for every positive one
  :param kwargs:
      Additional keyword-based parameters passed to TrainingLoop.__init__
docstr_tok: ["Initialize","the","training","loop",".",":","param","negative_sampler",":","The","class",",","instance",",","or","name","of","the","negative","sampler",":","param","negative_sampler_kwargs",":","Keyword","arguments","to","pass","to","the","negative","sampler","class","on","instantiation","for","every","positive","one",":","param","kwargs",":","Additional","keyword-based","parameters","passed","to","TrainingLoop.__init__"]
code: def __init__(
      self,
      negative_sampler: HintOrType[NegativeSampler] = None,
      negative_sampler_kwargs: OptionalKwargs = None,
      **kwargs,
  ):
      """Initialize the training loop.
  
      :param negative_sampler: The class, instance, or name of the negative sampler
      :param negative_sampler_kwargs: Keyword arguments to pass to the negative sampler class on instantiation
          for every positive one
      :param kwargs:
          Additional keyword-based parameters passed to TrainingLoop.__init__
      """
      super().__init__(**kwargs)
      self.negative_sampler = negative_sampler
      self.negative_sampler_kwargs = negative_sampler_kwargs
  
imports: import logging
  from typing import Optional
  import torch.utils.data
  from class_resolver import HintOrType, OptionalKwargs
  from torch.utils.data import DataLoader
  from .training_loop import TrainingLoop
  from ..losses import Loss
  from ..models.base import Model
  from ..sampling import NegativeSampler
  from ..triples import CoreTriplesFactory
  from ..triples.instances import SLCWABatch, SLCWASampleType
  from ..typing import InductiveMode
label_desc: Use image node_id 1 to create a new SLCWATrainingLoop object from inherited base classes: TrainingLoop with example: obj = SLCWATrainingLoop(negative_sampler, negative_sampler_kwargs)