code: def forward(self, x):
      x = torch.unsqueeze(x, 2)
      x = torch.ge(x, self.ge_tensor)
      x = x.float()
      x = torch.sum(x, dim=2, keepdim=False)
  
      if self.n_bins is not None:
          # Clipping the encoded values (Needed for sklearn).
          x = torch.min(self.n_bins, x)
  
      if self.encode in ["onehot-dense", "onehot"]:
          x = self.ohe(x)
  
      return x
  
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 2 for calling the KBinsDiscretizer obj's underlying member method code with example usage: obj.forward(x) and returns: x