code: def __init__(
      self,
      input_dim=128,
      hidden_dim=128,
      output_dim=128,
      num_layers=4,
      **kwargs,
  ):
      self.input_dim = input_dim
      self.output_dim = output_dim
      self.hidden_dim = hidden_dim
      self.num_layers = num_layers
  
      layers = []
      layers.append(
          LayerSpec(torch.nn.Linear, self.input_dim, self.hidden_dim)
      )
      for x in range(self.num_layers):
          layers.append(
              LayerSpec(
                  torch.nn.Linear,
                  self.hidden_dim,
                  self.hidden_dim,
                  bias=False,
              )
          )
          layers.append(lambda x: x)
      layers.append(
          LayerSpec(torch.nn.Linear, self.hidden_dim, self.output_dim)
      )
  
      super().__init__(
          layers=layers, loss_fn=torch.nn.CrossEntropyLoss(), **kwargs
      )
  
imports: import os
  import json
  import argparse
  import torch
  from collections import OrderedDict
  from deepspeed.pipe import PipelineModule, LayerSpec
  from deepspeed.moe.layer import MoE
  from deepspeed.accelerator import get_accelerator
  import deepspeed.comm
label_desc: Use image node_id 1 to create a new LinearStackPipe object from inherited base classes: PipelineModule with example: obj = LinearStackPipe(input_dim, hidden_dim, output_dim, num_layers)