code: def __init__(
      self,
      logical_operator,
      classes,
      binarize,
      jll_calc_bias,
      feature_log_prob_minus_neg_prob,
      device,
  ):
      super(BernoulliNBModel, self).__init__(logical_operator)
      self.classification = True
      self.binarize = binarize
      self.jll_calc_bias = torch.nn.Parameter(
          torch.from_numpy(jll_calc_bias.astype("float64")).view(-1),
          requires_grad=False,
      )
      self.feature_log_prob_minus_neg_prob = torch.nn.Parameter(
          torch.from_numpy(
              feature_log_prob_minus_neg_prob.astype("float64")
          ),
          requires_grad=False,
      )
      self.classes = torch.nn.Parameter(
          torch.IntTensor(classes), requires_grad=False
      )
      self.perform_class_select = False
      if min(classes) != 0 or max(classes) != len(classes) - 1:
          self.perform_class_select = True
  
file_path: hummingbird/hummingbird/ml/operator_converters/_nb_implementations.py
imports: import torch
  from ._physical_operator import PhysicalOperator
label_desc: Use image node_id 1 to create a new BernoulliNBModel object from inherited base classes: PhysicalOperator, torch.nn with example: obj = BernoulliNBModel(logical_operator, classes, binarize, jll_calc_bias, feature_log_prob_minus_neg_prob, device)