code: def _layer_activations(
      layer: nn.Module, layer_args: List[Any], out: Any
  ) -> int:
      """
      Computes the number of activations produced by a single layer.
  
      Activations are counted only for convolutional and linear layers. To override this
      behavior, a layer can define a method to compute activations with the signature
      below, which will be used to compute the activations instead.
  
      Class MyModule(nn.Module):
          def activations(self, out, *layer_args):
              ...
      """
  
      typestr = layer.__repr__()
      if hasattr(layer, "activations"):
          activations = layer.activations(out, *layer_args)
      elif isinstance(
          layer, (nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d)
      ):
          activations = out.numel()
      else:
          return 0
  
      message = [f"module: {typestr}", f"activations: {activations}"]
      logging.debug("\t".join(message))
      return int(activations)
  
imports: import collections.abc
  import logging
  import operator
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from classy_vision.generic.util import eval_model, get_batchsize_per_replica, get_model_dummy_input, is_leaf, is_on_gpu
  from torch.cuda import cudart
label_desc: Use image node_id 4 for calling a global function with example usage: _layer_activations(layer, layer_args, out) and returns: int, int