class_docstr: The SETR Multi-Level feature Aggregation Head implementation.
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone_indices (tuple): A tuple indicates the indices of output of backbone.
          It can be either one or two values, if two values, the first index will be taken as
          a deep-supervision feature in auxiliary layer; the second one will be taken as
          input of pixel representation. If one value, it is taken by both above.
      in_channels (int): The number of input channels. Default: 10.
      mla_channels (int, optional): The number of middle channels of ConvMLA Layer. Default: 256.
      mlahead_channels (int, optional): The number of middle channels of mla head. Default: 128.
      lr_multiple (int, optional): The leanring rate multiple of head parameters. Default: 10.
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code: def forward(self, x, _shape):
      logits = []
  
      feats = [x[_idx] for _idx in self.backbone_indices]
  
      for i in range(self.mla_feat_nums):
          feats[i] = (
              self.norms[i](feats[i])
              .transpose([0, 2, 1])
              .reshape([0, self.in_channels, _shape[2], _shape[3]])
          )
  
      feats = self.mla(feats)
      if self.training:
          for i in range(self.mla_feat_nums):
              logits.append(self.aux_heads[i](feats[i]))
  
      for i in range(self.mla_feat_nums):
          feats[i] = self.feat_convs[i](feats[i])
  
      feat_mix = paddle.concat(feats, axis=1)
      logits.insert(0, self.cls_head(feat_mix))
  
      return logits
  
imports: import paddle.nn
  import paddle.nn.functional
  import paddle
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the MLAHead obj's underlying member method code with example usage: obj.forward(x, _shape) and returns: logits