code: def _build_features_network():
      layers = []
  
      for input_dim, output_dim in [(1, 64), (64, 128)]:
          layers += [
              torch.nn.Conv2d(
                  input_dim,
                  output_dim,
                  kernel_size=(3, 3),
                  stride=(1, 1),
                  padding=(1, 1),
              ),
              torch.nn.ReLU(inplace=True),
              torch.nn.MaxPool2d(
                  kernel_size=2,
                  stride=2,
                  padding=0,
                  dilation=1,
                  ceil_mode=False,
              ),
          ]
  
      for input_dim, output_dim in [(128, 256), (256, 512)]:
          layers += [
              torch.nn.Conv2d(
                  input_dim,
                  output_dim,
                  kernel_size=(3, 3),
                  stride=(1, 1),
                  padding=(1, 1),
              ),
              torch.nn.ReLU(inplace=True),
              torch.nn.Conv2d(
                  output_dim,
                  output_dim,
                  kernel_size=(3, 3),
                  stride=(1, 1),
                  padding=(1, 1),
              ),
              torch.nn.ReLU(inplace=True),
              torch.nn.MaxPool2d(
                  kernel_size=2,
                  stride=2,
                  padding=0,
                  dilation=1,
                  ceil_mode=False,
              ),
          ]
  
      return torch.nn.Sequential(*layers)
  
file_path: audio/src/torchaudio/prototype/pipelines/_vggish/_vggish_impl.py
imports: import math
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: _build_features_network() and returns: torch