code: def forward(self, x, attention_mask, head_mask=None):
      query = self.query(x)
      key = self.key(x)
      value = self.value(x)
  
      query_dim1, query_dim2 = paddle.shape(query)[:-1]
      new_shape = [
          query_dim1,
          query_dim2,
          self.num_attention_heads,
          self.attention_head_size,
      ]
      query = query.reshape(new_shape).transpose(perm=(0, 2, 1, 3))
      key = key.reshape(new_shape).transpose(perm=(0, 2, 3, 1))
      value = value.reshape(new_shape).transpose(perm=(0, 2, 1, 3))
  
      attention = paddle.matmul(query, key) / math.sqrt(
          self.attention_head_size
      )
      attention = attention + attention_mask
      attention_value = F.softmax(attention, axis=-1)
      attention_value = self.dropout(attention_value)
  
      if head_mask is not None:
          attention_value = attention_value * head_mask
  
      context = paddle.matmul(attention_value, value).transpose(
          perm=(0, 2, 1, 3)
      )
      ctx_dim1, ctx_dim2 = paddle.shape(context)[:-2]
      new_context_shape = [
          ctx_dim1,
          ctx_dim2,
          self.all_head_size,
      ]
      context = context.reshape(new_context_shape)
  
      if self.output_attentions:
          return (context, attention_value)
      else:
          return (context,)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn import ReLU, Swish, GELU
  import math
  from ppdet.core.workspace import register
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 2 for calling the BertSelfAttention obj's underlying member method code with example usage: obj.forward(x, attention_mask, head_mask) and returns: context,  attention_value, context