code: def __init__(
      self,
      input_shape,
      activation=torch.nn.ReLU,
      dropout=0.15,
      cnn_channels=[64, 128],
      rnn_class=sb.nnet.RNN.LSTM,
      rnn_layers=4,
      rnn_neurons=512,
      rnn_bidirectional=True,
      rnn_re_init=False,
      projection_neurons=512,
  ):
      super().__init__(input_shape=input_shape)
  
      self.append(sb.nnet.containers.Sequential, layer_name="VGG")
  
      self.append(
          sb.nnet.CNN.Conv2d,
          out_channels=cnn_channels[0],
          kernel_size=(3, 3),
          layer_name="conv_1_1",
      )
      self.append(activation(), layer_name="act_1_1")
      self.append(
          sb.nnet.CNN.Conv2d,
          out_channels=cnn_channels[0],
          kernel_size=(3, 3),
          layer_name="conv_1_2",
      )
      self.append(activation(), layer_name="act_1_2")
      self.append(
          sb.nnet.pooling.Pooling2d(
              pool_type="max",
              kernel_size=(2, 2),
              pool_axis=(1, 2),
          ),
          layer_name="pooling_1",
      )
  
      self.append(
          sb.nnet.CNN.Conv2d,
          out_channels=cnn_channels[1],
          kernel_size=(3, 3),
          layer_name="conv_2_1",
      )
      self.append(activation(), layer_name="act_2_1")
      self.append(
          sb.nnet.CNN.Conv2d,
          out_channels=cnn_channels[1],
          kernel_size=(3, 3),
          layer_name="conv_2_2",
      )
      self.append(activation(), layer_name="act_2_2")
      self.append(
          sb.nnet.pooling.Pooling2d(
              pool_type="max",
              kernel_size=(2, 2),
              pool_axis=(1, 2),
          ),
          layer_name="pooling_2",
      )
  
      if rnn_layers > 0:
          self.append(
              rnn_class,
              layer_name="RNN",
              hidden_size=rnn_neurons,
              num_layers=rnn_layers,
              dropout=dropout,
              bidirectional=rnn_bidirectional,
              re_init=rnn_re_init,
          )
  
      self.append(
          sb.nnet.linear.Linear,
          n_neurons=projection_neurons,
          layer_name="proj",
      )
      self.append(torch.nn.Tanh(), layer_name="proj_act")
  
imports: import torch
  import speechbrain
label_desc: Use image node_id 1 to create a new ESPnetVGG object from inherited base classes: sb.nnet.containers with example: obj = ESPnetVGG(input_shape, activation, dropout, cnn_channels, rnn_class, rnn_layers, rnn_neurons, rnn_bidirectional, rnn_re_init, projection_neurons)