code: def forward(self, x, padding_mask, alibi_bias, alibi_scale):
      if self.norm is not None and not self.layer_norm_first:
          x = self.norm(x)
  
      x = self.dropout(x)
  
      for i, blk in enumerate(self.blocks):
          if (
              not self.training
              or self.layerdrop == 0
              or (np.random.random() > self.layerdrop)
          ):
              ab = alibi_bias
              if ab is not None and alibi_scale is not None:
                  scale = (
                      alibi_scale[i]
                      if alibi_scale.size(0) > 1
                      else alibi_scale.squeeze(0)
                  )
                  ab = ab * scale.type_as(ab)
              x, _ = blk(x, padding_mask, ab)
  
      if self.norm is not None and self.layer_norm_first:
          x = self.norm(x)
  
      return x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from dataclasses import dataclass
  from fairseq.modules import LayerNorm, SamePad, SamePad2d, TransposeLast
label_desc: Use image node_id 2 for calling the BlockEncoder obj's underlying member method code with example usage: obj.forward(x, padding_mask, alibi_bias, alibi_scale) and returns: x