class_docstr: Class implementing the GEMM strategy in PyTorch for tree-base models.
code: def forward(self, x):
      x = x.t()
      x = self.decision_cond(torch.mm(self.weight_1, x), self.bias_1)
      x = x.view(self.n_trees, self.hidden_one_size, -1)
      x = x.float()
  
      x = torch.matmul(self.weight_2, x)
  
      x = x.view(self.n_trees * self.hidden_two_size, -1) == self.bias_2
      x = x.view(self.n_trees, self.hidden_two_size, -1)
      if self.tree_op_precision_dtype == "float32":
          x = x.float()
      else:
          x = x.double()
  
      x = torch.matmul(self.weight_3, x)
      x = x.view(self.n_trees, self.hidden_three_size, -1)
  
      x = self.aggregation(x)
  
      if self.regression:
          return x
  
      if self.anomaly_detection:
          # Select the class (-1 if negative) and return the score.
          return (
              torch.where(
                  x.view(-1) < 0, self.classes[0], self.classes[1]
              ),
              x,
          )
  
      if self.perform_class_select:
          return (
              torch.index_select(
                  self.classes, 0, torch.argmax(x, dim=1)
              ),
              x,
          )
      else:
          return torch.argmax(x, dim=1), x
  
imports: from abc import abstractmethod
  from enum import Enum
  import numpy
  import torch
  from .None import constants
  from ._physical_operator import PhysicalOperator
  from .None import _tree_commons
label_desc: Use image node_id 3 for calling the GEMMTreeImpl obj's underlying member method code with example usage: obj.forward(x) and returns: x, torch,  x, torch,  x, torch,  x