class_docstr: Megatron Vision Transformer Model.
class_docstr_tok: ["Megatron","Vision","Transformer","Model","."]
docstr: Our dataloaders produce a micro-batch and then we fetch
  a number of microbatches depending on the global batch size and model parallel size
  from the dataloader to produce a list of microbatches.
  The list of microbatches is then piped through the pipeline using megatron-core fwd/bwd functions.
docstr_tok: ["Our","dataloaders","produce","a","micro-batch","and","then","we","fetch","a","number","of","microbatches","depending","on","the","global","batch","size","and","model","parallel","size","from","the","dataloader","to","produce","a","list","of","microbatches",".","The","list","of","microbatches","is","then","piped","through","the","pipeline","using","megatron-core","fwd\/bwd","functions","."]
code: def validation_step(self, dataloader_iter, batch_idx):
      """
      Our dataloaders produce a micro-batch and then we fetch
      a number of microbatches depending on the global batch size and model parallel size
      from the dataloader to produce a list of microbatches.
      The list of microbatches is then piped through the pipeline using megatron-core fwd/bwd functions.
      """
      mode = "test" if self.trainer.testing else "val"
  
      # Initialize userbuffer communicators.
      if self.initialize_ub:
          self.initialize_ub_func()
  
      loss, accuracy = self.fwd_bwd_step(
          dataloader_iter, batch_idx, True
      )
  
      self.validation_step_outputs.append(
          (loss, accuracy)
      ) if mode == "val" else self.test_step_outputs.append(
          (loss, accuracy)
      )
      return loss, accuracy
  
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 16 for calling the MegatronVitClassificationModel obj's underlying member method code with example usage: obj.validation_step(dataloader_iter, batch_idx) and returns: loss,  accuracy