code: def sequence_dataloader(
      model,
      total_samples,
      hidden_dim,
      device,
      seq_len: int = 32,
      dtype=torch.half,
  ):
      batch_size = model.train_micro_batch_size_per_gpu()
      train_data = torch.randn(
          total_samples, seq_len, hidden_dim, device=device, dtype=dtype
      )
      train_label = torch.empty(
          total_samples, dtype=torch.long, device=device
      ).random_(hidden_dim)
      train_dataset = torch.utils.data.TensorDataset(
          train_data, train_label
      )
      train_loader = torch.utils.data.DataLoader(
          train_dataset, batch_size=batch_size
      )
      return train_loader
  
imports: import os
  import json
  import argparse
  import torch
  from deepspeed.pipe import PipelineModule, LayerSpec
  from deepspeed.moe.layer import MoE
  import deepspeed.comm
label_desc: Use image node_id 3 for calling a global function with example usage: sequence_dataloader(model, total_samples, hidden_dim, device, seq_len, dtype) and returns: train_loader