class_docstr: Dataloader for the Jigsaw Unintended Bias in Toxicity Classification.
  Source: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/
class_docstr_tok: ["Dataloader","for","the","Jigsaw","Unintended","Bias","in","Toxicity","Classification",".","Source",":","https",":","\/\/www.kaggle.com\/c\/jigsaw-unintended-bias-in-toxicity-classification\/"]
code: def __init__(
      self,
      train_csv_file="jigsaw_data/train.csv",
      test_csv_file="jigsaw_data/test.csv",
      train=True,
      compute_bias_weights=True,
      loss_weight=0.75,
      classes=["toxic"],
      identity_classes=["female"],
      soft_labels=False,
  ):
      self.classes = classes
      self.soft_labels = soft_labels
      self.identity_classes = identity_classes
  
      super().__init__(
          train_csv_file=train_csv_file,
          test_csv_file=test_csv_file,
          train=train,
      )
      if train:
          if compute_bias_weights:
              self.weights = self.compute_weigths(self.train_set_pd)
          else:
              self.weights = None
  
      self.train = train
      self.loss_weight = loss_weight
  
imports: import datasets
  import numpy
  import pandas
  import torch
  from torch.utils.data.dataset import Dataset
  from tqdm import tqdm
label_desc: Use image node_id 1 to create a new JigsawDataBias object from inherited base classes: JigsawData with example: obj = JigsawDataBias(train_csv_file, test_csv_file, train, compute_bias_weights, loss_weight, classes, identity_classes, soft_labels)