code: def get_optimizer_parameters(self, config):
      # For finetuning setup, we have classifier
      lr = config.optimizer.params.lr
      model_config = config.model_config.get(config.model, {})
      finetune_lr_multiplier = model_config.get(
          "finetune_lr_multiplier", 1
      )
      # Finetune the bert pretrained part with finetune_lr_multiplier if it is set
      parameters = get_bert_configured_parameters(
          self.base, lr * finetune_lr_multiplier
      )
      parameters += get_bert_configured_parameters(self.classifier, lr)
      return parameters
  
imports: import collections
  from copy import deepcopy
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.encoders import MultiModalEncoderBase
  from mmf.utils.build import build_classifier_layer
  from mmf.utils.modeling import get_bert_configured_parameters
label_desc: Use image node_id 4 for calling the ConcatBERT obj's underlying member method code with example usage: obj.get_optimizer_parameters(config) and returns: parameters