code: def forward(self, x):
      x = x.permute(0, 3, 1, 2).contiguous()
      x = self.base(x)
  
      outputs = []
      for conv in self.convs:
          x = conv(x)
          x = F.gelu(x)
          outputs.append(x)
      outputs = torch.cat(outputs, dim=1)
      outputs = outputs.permute(0, 2, 3, 1).contiguous()
      return outputs
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from transformers import AutoModel
label_desc: Use image node_id 2 for calling the ConvolutionLayer obj's underlying member method code with example usage: obj.forward(x) and returns: outputs