code: def __init__(
      self, logical_operator, encode, n_bins, bin_edges, labels, device
  ):
      super(KBinsDiscretizer, self).__init__(logical_operator)
      self.transformer = True
      self.encode = encode
  
      self.ge_tensor = torch.FloatTensor(bin_edges[:, 1:-1])
      self.ohe = OneHotEncoder(logical_operator, labels, device)
      if n_bins is not None:
          self.n_bins = torch.FloatTensor([[n - 1 for n in n_bins]])
      else:
          self.n_bins = None
  
file_path: hummingbird/hummingbird/ml/operator_converters/_discretizer_implementations.py
imports: import torch
  from ._physical_operator import PhysicalOperator
  from ._one_hot_encoder_implementations import OneHotEncoder
label_desc: Use image node_id 1 to create a new KBinsDiscretizer object from inherited base classes: PhysicalOperator, torch.nn with example: obj = KBinsDiscretizer(logical_operator, encode, n_bins, bin_edges, labels, device)