class_docstr: Megatron Vision Transformer Model.
class_docstr_tok: ["Megatron","Vision","Transformer","Model","."]
code: def fwd_bwd_step(self, dataloader_iter, batch_idx, forward_only):
      # handle asynchronous grad reduction
      no_sync_func = None
      grad_sync_func = None
      param_sync_func = None
      if not forward_only and self.with_distributed_adam:
          no_sync_func = partial(
              self._optimizer.no_sync,
              greedy_grad_copy=self.megatron_amp_O2,
          )
          grad_sync_func = self.reduce_overlap_gradients
          param_sync_func = self.sync_overlap_parameters
  
      # pipeline schedules will get these from self.model.config
      for module in self.get_module_list():
          module.config.no_sync_func = no_sync_func
          module.config.grad_sync_func = grad_sync_func
          module.config.param_sync_func = param_sync_func
  
      # run forward and backwards passes for an entire global batch
      # we do this inside training_step to support pipeline parallelism
      fwd_bwd_function = get_forward_backward_func()
  
      # TODO @akhattar: add num_micro_batches_with_partial_activation_checkpoints when ready
      losses_reduced_per_micro_batch = fwd_bwd_function(
          forward_step_func=self.get_forward_output_and_loss_func(),
          data_iterator=dataloader_iter,
          model=[self.model],
          num_microbatches=get_num_microbatches(),
          forward_only=forward_only,
          seq_length=self.cfg.encoder_seq_length,
          micro_batch_size=self.cfg.micro_batch_size,
      )
  
      # only the last stages of the pipeline return losses
      if losses_reduced_per_micro_batch:
          if (not forward_only) or self.cfg.data.get(
              "validation_drop_last", True
          ):
              # average loss across micro batches
              loss_tensors_list = [
                  loss_reduced["loss"]
                  for loss_reduced in losses_reduced_per_micro_batch
              ]
              loss_tensor = torch.stack(loss_tensors_list)
              loss_mean = loss_tensor.mean()
              acc_tensors_list = [
                  loss_reduced["accuracy"]
                  for loss_reduced in losses_reduced_per_micro_batch
              ]
              acc_tensor = torch.stack(acc_tensors_list)
              accuracy_mean = acc_tensor.mean()
          else:
              # Get the total loss since micro batches sizes are not uniform
              raise NotImplementedError(
                  "Losses of micro batches sizes must be uniform!"
              )
      else:
          # we're not on the last pipeline stage so no losses
          if forward_only:
              loss_mean = []
              accuracy_mean = []
          else:
              loss_mean = torch.tensor(0.0).cuda()
              accuracy_mean = loss_mean.copy()
  
      return loss_mean, accuracy_mean
  
imports: import itertools
  from functools import partial
  from typing import Any, Optional
  import torch
  from omegaconf.dictconfig import DictConfig
  from pytorch_lightning.accelerators import CPUAccelerator
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingSampler
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common.megatron.build_model import build_model
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module, MegatronModule
  from nemo.collections.nlp.modules.common.megatron.utils import average_losses_across_data_parallel_group, get_all_params_for_weight_decay_optimization, get_linear_layer, get_params_for_weight_decay_optimization, init_method_normal, scaled_init_method_normal
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank, torch_dtype_from_precision
  from nemo.collections.vision.data.megatron.data_samplers import MegatronVisionPretrainingRandomSampler
  from nemo.collections.vision.data.megatron.vit_dataset import build_train_valid_datasets
  from nemo.collections.vision.modules.vit.vit_backbone import VitBackbone, VitMlpHead
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 7 for calling the MegatronVitClassificationModel obj's underlying member method code with example usage: obj.fwd_bwd_step(dataloader_iter, batch_idx, forward_only) and returns: loss_mean,  accuracy_mean