docstr: Helper method for allreduce_sequence_parallel_gradients
docstr_tok: ["Helper","method","for","allreduce_sequence_parallel_gradients"]
code: def _append_sequence_parallel_module_grads(self, module, grads):
      """Helper method for allreduce_sequence_parallel_gradients"""
  
      for param in module.parameters():
          sequence_parallel_param = getattr(
              param, "sequence_parallel", False
          )
          if sequence_parallel_param and param.requires_grad:
              if self.megatron_amp_O2:
                  grad = param.main_grad
              else:
                  grad = param.grad
              grads.append(grad.data)
  
imports: from functools import partial
  from typing import Any, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from torch._inductor import config
  from nemo.collections.multimodal.data.dreambooth.dreambooth_dataset import DreamBoothDataset
  from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import DiagonalGaussianDistribution
  from nemo.collections.multimodal.parts.utils import randn_like
  from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingRandomSampler
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.core.classes.common import Serialization
  from nemo.utils import logging
label_desc: Use image node_id 10 for calling the MegatronDreamBooth obj's underlying member method code with example usage: obj._append_sequence_parallel_module_grads(module, grads) without return types