class_docstr: This is an implementation of transformer model with CNN pre-encoder for SE.
  
  Arguments
  ---------
  d_model : int
      The number of expected features in the encoder inputs.
  output_size : int
      The number of neurons in the output layer.
  output_activation : torch class
      The activation function of the output layer (default=ReLU).
  nhead : int
      The number of heads in the multi-head attention models (default=8).
  num_layers : int
      The number of sub-layers in the transformer (default=8).
  d_ffn : int
      The number of expected features in the encoder layers (default=512).
  dropout : int
      The dropout value (default=0.1).
  activation : torch class
      The activation function of intermediate layers (default=LeakyReLU).
  causal : bool
      True for causal setting, the model is forbidden to see future frames (default=True).
  custom_emb_module : torch class
      Module that processes the input features before the transformer model.
  
  Example
  -------
  >>> src = torch.rand([8, 120, 256])
  >>> net = CNNTransformerSE(d_model=256, output_size=257)
  >>> out = net(src)
  >>> out.shape
  torch.Size([8, 120, 257])
docstr: Processes the input tensor x and returns an output tensor.
docstr_tok: ["Processes","the","input","tensor","x","and","returns","an","output","tensor","."]
code: def forward(self, x, src_key_padding_mask=None):
      """Processes the input tensor x and returns an output tensor."""
      if self.causal:
          self.attn_mask = get_lookahead_mask(x)
      else:
          self.attn_mask = None
  
      if self.custom_emb_module is not None:
          x = self.custom_emb_module(x)
  
      encoder_output, _ = self.encoder(
          src=x,
          src_mask=self.attn_mask,
          src_key_padding_mask=src_key_padding_mask,
      )
  
      output = self.output_layer(encoder_output)
      output = self.output_activation(output)
  
      return output
  
imports: import torch
  from torch import nn
  from speechbrain.nnet.linear import Linear
  from speechbrain.lobes.models.transformer.Transformer import TransformerInterface, get_lookahead_mask
label_desc: Use image node_id 2 for calling the CNNTransformerSE obj's underlying member method code with example usage: obj.forward(x, src_key_padding_mask) and returns: output