code: def forward(self, x, mask=None):
      residual = self.self_attn(x, mask=mask)
      residual = torch.cat(residual, dim=-1)
      residual = self.residual_dropout_attn(residual)
      x = self.norm_attn(x + residual)
      residual = self.linear2(
          self.ff_dropout(self.activation(self.linear1(x)))
      )
      residual = torch.cat(residual, dim=-1)
      residual = self.residual_dropout_ff(residual)
      x = self.norm_ff(x + residual)
      return x
  
imports: import copy
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from stanza.models.constituency.positional_encoding import ConcatSinusoidalEncoding
label_desc: Use image node_id 2 for calling the PartitionedTransformerEncoderLayer obj's underlying member method code with example usage: obj.forward(x, mask) and returns: x