code: def forward(self, mla_p2, mla_p3, mla_p4, mla_p5):
      head2 = F.interpolate(
          self.head2(mla_p2),
          size=(4 * mla_p2.shape[3], 4 * mla_p2.shape[3]),
          mode="bilinear",
          align_corners=True,
      )
      head3 = F.interpolate(
          self.head3(mla_p3),
          size=(4 * mla_p3.shape[3], 4 * mla_p3.shape[3]),
          mode="bilinear",
          align_corners=True,
      )
      head4 = F.interpolate(
          self.head4(mla_p4),
          size=(4 * mla_p4.shape[3], 4 * mla_p4.shape[3]),
          mode="bilinear",
          align_corners=True,
      )
      head5 = F.interpolate(
          self.head5(mla_p5),
          size=(4 * mla_p5.shape[3], 4 * mla_p5.shape[3]),
          mode="bilinear",
          align_corners=True,
      )
  
      return paddle.concat([head2, head3, head4, head5], axis=1)
  
file_path: PaddleSeg/paddleseg/models/mla_transformer.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the MLAHeads obj's underlying member method code with example usage: obj.forward(mla_p2, mla_p3, mla_p4, mla_p5) and returns: paddle