code: def test(
      self, param_sz: int, n_layers: int, init_context_manager: bool
  ) -> None:
      class ManyParamModel(Module):
          def __init__(self) -> None:
              super().__init__()
  
              self.modulelist = ModuleList(
                  EltwiseMultiplicationModule(
                      weight=Parameter(
                          torch.empty((param_sz,), dtype=torch.float32)
                      )
                  )
                  for _ in range(n_layers)
              )
  
              for layer_num, module in enumerate(self.modulelist):
                  with deepspeed.zero.GatheredParameters(
                      module.weight, modifier_rank=0
                  ):
                      param: Parameter = module.weight
                      partition_sz = math.ceil(
                          param.numel() / dist.get_world_size()
                      )
                      offset = 0
                      for rank in range(dist.get_world_size()):
                          with torch.no_grad():
                              param[
                                  offset : offset + partition_sz
                              ].fill_(2 * layer_num * rank)
                          offset += partition_sz
  
          def forward(self, x: Tensor) -> Tensor:
              activations = []
  
              for module in self.modulelist:
                  x = module(x)
                  activations.append(x)
  
              return activations
  
      ds_cfg = {
          "train_micro_batch_size_per_gpu": 1,
          "zero_optimization": {
              "stage": 3,
              "stage3_max_reuse_distance": 0,
              "contiguous_gradients": True,
              "overlap_comm": True,
          },
          "optimizer": {"type": "Adam", "params": {"lr": 1.0}},
          "fp16": {
              "enabled": True,
              "loss_scale": 1.0,
          },
      }
  
      with deepspeed.zero.Init(
          config=ds_cfg,
          mem_efficient_linear=False,
          enabled=init_context_manager,
      ):
          model = ManyParamModel()
  
      ds_engine = _ds_initialize_for_param_partitioning_testing(
          model, ds_cfg
      )
  
      for _ in range(
          3
      ):  # test multiple iterations to cover prefetching
          activations: List[Tensor] = ds_engine(
              torch.ones(
                  (param_sz,),
                  dtype=torch.float16,
                  device=ds_engine.device,
              )
          )
          assert len(activations) == n_layers
  
          partition_sz = math.ceil(param_sz / self.world_size)
          expected_activations = torch.empty(
              param_sz, dtype=torch.float16, device=ds_engine.device
          )
          for start_idx in range(0, param_sz, partition_sz):
              expected_activations[
                  start_idx : start_idx + partition_sz
              ] = dist.get_rank()
  
          for layer_num, activation in enumerate(activations):
              expected_activations *= 2 * layer_num
              assert torch.allclose(activation, expected_activations)
  
          # TODO. finish writing this test
          ds_engine.backward(activations[-1].sum())
  
          avgd_gradients = ds_engine.optimizer.averaged_gradients
          assert set(avgd_gradients.keys()) == {
              0
          }, "should only have one parameter group"
          weight_gradients: List[Tensor] = avgd_gradients[0]
  
          for layer_num, activation in enumerate(weight_gradients):
              pass
  
imports: import math
  from typing import Dict, List, Set
  import pytest
  import deepspeed.comm
  import torch
  from torch import Tensor
  from torch.nn import Linear, Module
  from torch.nn.modules.container import ModuleList
  from torch.nn.modules.loss import L1Loss
  from torch.nn.parameter import Parameter
  from unit.common import DistributedTest
  from unit.simple_model import SimpleModel, random_dataloader
  import deepspeed
  from deepspeed.runtime.engine import DeepSpeedEngine
  from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
  from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
label_desc: Use image node_id 1 for calling the TestZero3ParamPartitioningManyParams obj's underlying member method code with example usage: obj.test(param_sz, n_layers, init_context_manager) and returns: activations