class_docstr: This class is adapted from the compacter implementation at https://github.com/rabeehk/compacter
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      if self.shared_W_phm:
          parameters = ForwardContext.get_context().shared_parameters[
              self.name
          ]
          if self.factorized_phm_W:
              W = torch.bmm(
                  parameters[f"W_{self.position}_left"],
                  parameters[f"W_{self.position}_right"],
              )
          else:
              W = parameters[f"W_{self.position}"]
      else:
          if self.factorized_phm_W:
              W = torch.bmm(self.W_left, self.W_right)
          else:
              W = self.W
      if self.shared_phm_rule:
          parameters = ForwardContext.get_context().shared_parameters[
              self.name
          ]
          if self.factorized_phm_rule:
              phm_rule = torch.bmm(
                  parameters["phm_rule_left"],
                  parameters["phm_rule_right"],
              )
          else:
              phm_rule = parameters["phm_rule"]
      else:
          if self.factorized_phm_rule:
              phm_rule = torch.bmm(
                  self.phm_rule_left, self.phm_rule_right
              )
          else:
              phm_rule = self.phm_rule
  
      H = kronecker_product(phm_rule, W).sum(0)
  
      y = torch.matmul(input=x, other=H)
      if self.b is not None:
          y += self.b
      return y
  
imports: import math
  import torch
  from torch import nn
  from transformers.activations import get_activation
  from ..configuration import AdapterFusionConfig, BnConfig
  from ..context import ForwardContext
label_desc: Use image node_id 6 for calling the PHMLayer obj's underlying member method code with example usage: obj.forward(x) and returns: y