class_docstr: The model consists of a sparse part and a dense part.
  
  The dense part is an nn.Linear module that is replicated across all trainers using
  DistributedDataParallel. The sparse part has nn.EmbeddingBags stored on multiple
  parameter servers.
  
  The model holds a Remote Reference to the embedding tables on the parameter
  servers.
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code: def __init__(self, emb_rref_list, device):
      super().__init__()
      self.emb_rref_list = emb_rref_list
      fc1 = torch.nn.Linear(512, 256)
      fc2 = torch.nn.Linear(256, 128)
      relu = torch.nn.ReLU()
      fc3 = torch.nn.Linear(128, 64)
      fc4 = torch.nn.Linear(64, 32)
      fc5 = torch.nn.Linear(32, 8)
      sec = nn.Sequential(fc1, fc2, relu, fc3, fc4, fc5)
      self.ddp = DDP(sec.to(device), device_ids=[device])
      self.device = device
  
imports: import argparse
  import io
  import os
  import random
  import shlex
  import subprocess
  import time
  import numpy
  import torch
  import torch.nn
  import torch.distributed
  import torch.distributed.autograd
  import torch.distributed.rpc
  import torch.multiprocessing
  import torch.optim
  from torch.distributed.optim import DistributedOptimizer
  from torch.distributed.rpc import RRef, TensorPipeRpcBackendOptions
  from torch.distributed.rpc.backend_registry import BackendType
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 1 to create a new HybridModel object from inherited base classes: torch.nn with example: obj = HybridModel(emb_rref_list, device)