class_docstr: Megatron Vision Transformer Model.
class_docstr_tok: ["Megatron","Vision","Transformer","Model","."]
code: def get_forward_output_and_loss_func(self, validation_step=False):
      def loss_func(labels, output_tensor):
          logits = output_tensor.contiguous().float()
          loss = torch.nn.functional.cross_entropy(logits, labels)
  
          outputs = torch.argmax(logits, -1)
          correct = (outputs == labels).float()
          accuracy = torch.mean(correct)
  
          averaged_loss = average_losses_across_data_parallel_group(
              [loss, accuracy]
          )
  
          return loss, {
              "loss": averaged_loss[0],
              "accuracy": averaged_loss[1],
          }
  
      def fwd_output_and_loss_func(dataloader_iter, model):
          batch = next(dataloader_iter)
          if (
              parallel_state.get_pipeline_model_parallel_world_size()
              == 1
          ):
              batch = [x.cuda(non_blocking=True) for x in batch]
              tokens, labels = batch
          else:
              # Vision transformer doesn't need attention mask
              if parallel_state.is_pipeline_first_stage():
                  # Fist pipeline stage needs only the tokens and position_ids
                  tokens = batch[0].cuda(non_blocking=True)
                  labels = None
              elif parallel_state.is_pipeline_last_stage():
                  # Last pipeline stage needs only the labels and loss_mask
                  labels = batch[1].cuda(non_blocking=True)
                  tokens = None
              else:
                  # Intermediate pipeline stage doesn't need any inputs
                  tokens, labels = None, None
  
          output_tensor = model(tokens)
          return output_tensor, partial(loss_func, labels)
  
      return fwd_output_and_loss_func
  
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 14 for calling the MegatronVitClassificationModel obj's underlying member method code with example usage: obj.get_forward_output_and_loss_func(validation_step) and returns: fwd_output_and_loss_func, loss,  dict, output_tensor,  partial