class_docstr: Megatron Vision Transformer Model.
class_docstr_tok: ["Megatron","Vision","Transformer","Model","."]
code: def configure_optimizers(self):
      if self.with_distributed_adam:
          # Disable overlapped grad sync for layer norm grads when
          # sequence parallelism is enabled
          for param in self.parameters():
              if getattr(param, "sequence_parallel_enabled", False):
                  param._disable_greedy_grad_copy = (
                      not self.megatron_amp_O2
                  )
                  param._disable_overlap_grad_sync = True
  
          # KJJ - Copied this entire block, up to "return" here blindly from megatron_gpt_model.py
  
          # Initialize parameter buckets for overlapped grad and param syncs
          # Note: Params with disabled overlapping are put in the
          # last param bucket
          buckets = []
          if (
              self.cfg.get("virtual_pipeline_model_parallel_size", None)
              is not None
          ):
              # Initialize a bucket for each virtual pipeline stage
              for module in self.model:
                  if isinstance(module, Float16Module):
                      module = module.module
                  stage_bucket = []
                  # for layer in module.language_model.encoder.layers:
                  for layer in module.backbone.transformer.layers:
                      stage_bucket.extend(
                          p
                          for p in layer.parameters()
                          if not getattr(
                              p, "_disable_overlap_grad_sync", False
                          )
                      )
                  buckets.append(stage_bucket)
          else:
              # Initialize a bucket for each Transformer layer
              modules = (
                  self.model
                  if isinstance(self.model, list)
                  else [self.model]
              )
              for module in modules:
                  if isinstance(module, Float16Module):
                      module = module.module
                  # for layer in module.language_model.encoder.layers:
                  for layer in module.backbone.transformer.layers:
                      buckets.append(
                          [
                              p
                              for p in layer.parameters()
                              if not getattr(
                                  p, "_disable_overlap_grad_sync", False
                              )
                          ]
                      )
          buckets.reverse()
          used_params = set()
          for bucket in buckets:
              used_params.update(bucket)
          buckets[-1].extend(
              p for p in self.parameters() if p not in used_params
          )
          self.distributed_adam_buckets = buckets
  
      return super().configure_optimizers()
  
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 5 for calling the MegatronVitClassificationModel obj's underlying member method code with example usage: obj.configure_optimizers() and returns: super