code: def drop_connect(
      inputs, training: bool = False, drop_connect_rate: float = 0.0
  ):
      """Apply drop connect."""
      if not training:
          return inputs
  
      keep_prob = 1 - drop_connect_rate
      random_tensor = keep_prob + torch.rand(
          (inputs.size()[0], 1, 1, 1),
          dtype=inputs.dtype,
          device=inputs.device,
      )
      random_tensor.floor_()  # binarize
      output = inputs.div(keep_prob) * random_tensor
      return output
  
imports: import re
  from copy import deepcopy
  from .conv2d_layers import *
  from geffnet.activations import *
label_desc: Use image node_id 7 for calling a global function with example usage: drop_connect(inputs, training, drop_connect_rate) and returns: output, inputs