code: def _layer_flops(
      layer: nn.Module, layer_args: List[Any], y: Any
  ) -> int:
      """
      Computes the number of FLOPs required for a single layer.
  
      For common layers, such as Conv1d, the flop compute is implemented in this
      centralized place.
      For other layers, if it defines a method to compute flops with the signature
      below, we will use it to compute flops.
  
      Class MyModule(nn.Module):
          def flops(self, x):
              ...
  
      """
  
      x = layer_args[0]
      # get layer type:
      typestr = layer.__repr__()
      layer_type = typestr[: typestr.find("(")].strip()
      batchsize_per_replica = get_batchsize_per_replica(x)
  
      flops = None
      # 1D convolution:
      if layer_type in ["Conv1d"]:
          # x shape is N x C x W
          out_w = int(
              (
                  x.size()[2]
                  + 2 * layer.padding[0]
                  - layer.kernel_size[0]
              )
              / layer.stride[0]
              + 1
          )
          flops = (
              batchsize_per_replica
              * layer.in_channels
              * layer.out_channels
              * layer.kernel_size[0]
              * out_w
              / layer.groups
          )
      # 2D convolution:
      elif layer_type in ["Conv2d"]:
          out_h = int(
              (
                  x.size()[2]
                  + 2 * layer.padding[0]
                  - layer.kernel_size[0]
              )
              / layer.stride[0]
              + 1
          )
          out_w = int(
              (
                  x.size()[3]
                  + 2 * layer.padding[1]
                  - layer.kernel_size[1]
              )
              / layer.stride[1]
              + 1
          )
          flops = (
              batchsize_per_replica
              * layer.in_channels
              * layer.out_channels
              * layer.kernel_size[0]
              * layer.kernel_size[1]
              * out_h
              * out_w
              / layer.groups
          )
  
      # learned group convolution:
      elif layer_type in ["LearnedGroupConv"]:
          conv = layer.conv
          out_h = int(
              (x.size()[2] + 2 * conv.padding[0] - conv.kernel_size[0])
              / conv.stride[0]
              + 1
          )
          out_w = int(
              (x.size()[3] + 2 * conv.padding[1] - conv.kernel_size[1])
              / conv.stride[1]
              + 1
          )
          count1 = _layer_flops(layer.relu, x) + _layer_flops(
              layer.norm, x
          )
          count2 = (
              batchsize_per_replica
              * conv.in_channels
              * conv.out_channels
              * conv.kernel_size[0]
              * conv.kernel_size[1]
              * out_h
              * out_w
              / layer.condense_factor
          )
          flops = count1 + count2
  
      # non-linearities:
      elif layer_type in [
          "ReLU",
          "ReLU6",
          "Tanh",
          "Sigmoid",
          "Softmax",
          "SiLU",
      ]:
          flops = x.numel()
  
      # 2D pooling layers:
      elif layer_type in ["AvgPool2d", "MaxPool2d"]:
          in_h = x.size()[2]
          in_w = x.size()[3]
          if isinstance(layer.kernel_size, int):
              layer.kernel_size = (layer.kernel_size, layer.kernel_size)
          kernel_ops = layer.kernel_size[0] * layer.kernel_size[1]
          out_h = 1 + int(
              (in_h + 2 * layer.padding - layer.kernel_size[0])
              / layer.stride
          )
          out_w = 1 + int(
              (in_w + 2 * layer.padding - layer.kernel_size[1])
              / layer.stride
          )
          flops = x.size()[0] * x.size()[1] * out_w * out_h * kernel_ops
  
      # adaptive avg pool2d
      # This is approximate and works only for downsampling without padding
      # based on aten/src/ATen/native/AdaptiveAveragePooling.cpp
      elif layer_type in ["AdaptiveAvgPool2d"]:
          in_h = x.size()[2]
          in_w = x.size()[3]
          if isinstance(layer.output_size, int):
              out_h, out_w = layer.output_size, layer.output_size
          elif len(layer.output_size) == 1:
              out_h, out_w = layer.output_size[0], layer.output_size[0]
          else:
              out_h, out_w = layer.output_size
          if out_h > in_h or out_w > in_w:
              raise ClassyProfilerNotImplementedError(layer)
          batchsize_per_replica = x.size()[0]
          num_channels = x.size()[1]
          kh = in_h - out_h + 1
          kw = in_w - out_w + 1
          kernel_ops = kh * kw
          flops = (
              batchsize_per_replica
              * num_channels
              * out_h
              * out_w
              * kernel_ops
          )
  
      # linear layer:
      elif layer_type in ["Linear"]:
          weight_ops = layer.weight.numel()
          bias_ops = layer.bias.numel() if layer.bias is not None else 0
          flops = (
              (x.numel() / x.size(-1)) if x.ndim > 2 else x.size(0)
          ) * (weight_ops + bias_ops)
  
      # batch normalization / layer normalization:
      elif layer_type in [
          "BatchNorm1d",
          "BatchNorm2d",
          "BatchNorm3d",
          "SyncBatchNorm",
          "LayerNorm",
      ]:
          flops = 2 * x.numel()
  
      # 3D convolution
      elif layer_type in ["Conv3d"]:
          out_t = int(
              (
                  x.size()[2]
                  + 2 * layer.padding[0]
                  - layer.kernel_size[0]
              )
              // layer.stride[0]
              + 1
          )
          out_h = int(
              (
                  x.size()[3]
                  + 2 * layer.padding[1]
                  - layer.kernel_size[1]
              )
              // layer.stride[1]
              + 1
          )
          out_w = int(
              (
                  x.size()[4]
                  + 2 * layer.padding[2]
                  - layer.kernel_size[2]
              )
              // layer.stride[2]
              + 1
          )
          flops = (
              batchsize_per_replica
              * layer.in_channels
              * layer.out_channels
              * layer.kernel_size[0]
              * layer.kernel_size[1]
              * layer.kernel_size[2]
              * out_t
              * out_h
              * out_w
              / layer.groups
          )
  
      # 3D pooling layers
      elif layer_type in ["AvgPool3d", "MaxPool3d"]:
          in_t = x.size()[2]
          in_h = x.size()[3]
          in_w = x.size()[4]
          if isinstance(layer.kernel_size, int):
              layer.kernel_size = (
                  layer.kernel_size,
                  layer.kernel_size,
                  layer.kernel_size,
              )
          if isinstance(layer.padding, int):
              layer.padding = (
                  layer.padding,
                  layer.padding,
                  layer.padding,
              )
          if isinstance(layer.stride, int):
              layer.stride = (layer.stride, layer.stride, layer.stride)
          kernel_ops = (
              layer.kernel_size[0]
              * layer.kernel_size[1]
              * layer.kernel_size[2]
          )
          out_t = 1 + int(
              (in_t + 2 * layer.padding[0] - layer.kernel_size[0])
              / layer.stride[0]
          )
          out_h = 1 + int(
              (in_h + 2 * layer.padding[1] - layer.kernel_size[1])
              / layer.stride[1]
          )
          out_w = 1 + int(
              (in_w + 2 * layer.padding[2] - layer.kernel_size[2])
              / layer.stride[2]
          )
          flops = (
              batchsize_per_replica
              * x.size()[1]
              * out_t
              * out_h
              * out_w
              * kernel_ops
          )
  
      # adaptive avg pool3d
      # This is approximate and works only for downsampling without padding
      # based on aten/src/ATen/native/AdaptiveAveragePooling3d.cpp
      elif layer_type in ["AdaptiveAvgPool3d"]:
          in_t = x.size()[2]
          in_h = x.size()[3]
          in_w = x.size()[4]
          out_t = layer.output_size[0]
          out_h = layer.output_size[1]
          out_w = layer.output_size[2]
          if out_t > in_t or out_h > in_h or out_w > in_w:
              raise ClassyProfilerNotImplementedError(layer)
          batchsize_per_replica = x.size()[0]
          num_channels = x.size()[1]
          kt = in_t - out_t + 1
          kh = in_h - out_h + 1
          kw = in_w - out_w + 1
          kernel_ops = kt * kh * kw
          flops = (
              batchsize_per_replica
              * num_channels
              * out_t
              * out_w
              * out_h
              * kernel_ops
          )
  
      elif layer_type in ["Dropout", "Identity"]:
          flops = 0
  
      elif hasattr(layer, "flops"):
          # If the module already defines a method to compute flops with the signature
          # below, we use it to compute flops
          #
          #   Class MyModule(nn.Module):
          #     def flops(self, x):
          #       ...
          #   or
          #
          #   Class MyModule(nn.Module):
          #     def flops(self, x1, x2):
          #       ...
          flops = layer.flops(*layer_args)
  
      if flops is None:
          raise ClassyProfilerNotImplementedError(layer)
  
      message = [
          f"module type: {typestr}",
          f"input size: {get_shape(x)}",
          f"output size: {get_shape(y)}",
          f"params(M): {count_params(layer) / 1e6}",
          f"flops(M): {int(flops) / 1e6}",
      ]
      logging.debug("\t".join(message))
      return int(flops)
  
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 3 for calling a global function with example usage: _layer_flops(layer, layer_args, y) and returns: int