code: def forward(
      self,
      hidden_states,
      attention_mask,
      output_all_encoded_layers=True,
      checkpoint_activations=False,
  ):
      all_encoder_layers = []
  
      def custom(start, end):
          def custom_forward(*inputs):
              layers = self.layer[start:end]
              x_ = inputs[0]
              for layer in layers:
                  x_ = layer(x_, inputs[1])
              return x_
  
          return custom_forward
  
      if checkpoint_activations:
          raise NotImplementedError("`checkpoint` is not defined below")
          # l = 0
          # num_layers = len(self.layer)
          # chunk_length = math.ceil(math.sqrt(num_layers))
          # while l < num_layers:
          #    hidden_states = checkpoint.checkpoint(
          #        custom(
          #            l,  # noqa: F821
          #            l + chunk_length),
          #        hidden_states,
          #        attention_mask * 1)
          #    l += chunk_length
          # decoder layers
      else:
          for i, layer_module in enumerate(self.layer):
              hidden_states = layer_module(
                  hidden_states, attention_mask, grads=self.grads
              )
              hidden_states.register_hook(
                  lambda x, self=self: self.grads.append(
                      [x, "hidden_state"]
                  )
              )
  
              if output_all_encoded_layers:
                  all_encoder_layers.append(hidden_states)
  
      if not output_all_encoded_layers or checkpoint_activations:
          if self.pre_or_post:
              hidden_states = self.FinalLayerNorm(hidden_states)
          all_encoder_layers.append(hidden_states)
      return all_encoder_layers
  
imports: import numpy
  import torch
  import pytest
  import random
  import copy
  import os
  from torch import nn
  from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
  from deepspeed.accelerator import get_accelerator
  from unit.modeling import BertConfig, BertLayerNorm, BertEncoder
  from unit.modelingpreln import BertEncoder
  from unit.common import DistributedTest, is_rocm_pytorch
label_desc: Use image node_id 2 for calling the DSEncoder obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, output_all_encoded_layers, checkpoint_activations) and returns: all_encoder_layers, custom_forward, x_