code: def get_bias_dropout_add(training, fused):
      if fused:
          # jit scripting for a nn.module (with dropout) is not
          # triggering the fusion kernel. For now, we use two
          # different nn.functional routines to account for varying
          # dropout semantics during training and inference phases.
          if training:
              return bias_dropout_add_fused_train
          else:
              return bias_dropout_add_fused_inference
      else:
          return bias_dropout_add_unfused(training)
  
imports: from typing import Optional, Tuple
  import torch
label_desc: Use image node_id 5 for calling a global function with example usage: get_bias_dropout_add(training, fused) and returns: bias_dropout_add_unfused, bias_dropout_add_fused_train, bias_dropout_add_fused_inference