code: def build(self):
      self.base = FusionBase(self.config)
      num_features = self.config.num_features
      if not self._is_direct_features_input:
          num_features = (
              self.config.modal_encoder.params.num_output_features
          )
  
      # As the in_dim is dynamically calculated we need to copy classifier_config
      classifier_config = deepcopy(self.config.classifier)
      classifier_config.params.in_dim = (
          num_features * self.config.modal_hidden_size
      )
      classifier_config.params.in_dim += self.config.text_hidden_size
      self.classifier = build_classifier_layer(classifier_config)
  
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 3 for calling the ConcatBoW obj's underlying member method code with example usage: obj.build() without return types