class_docstr: interp_type can be 'nearest'/'bilinear'/'bilinear_like'
class_docstr_tok: ["interp_type","can","be","'nearest'\/'bilinear'\/'bilinear_like","'"]
code: def hybrid_forward(self, F, x, *args, **kwargs):
      x = self.branches[0](x)
      if self.num_branches == 1:
          return [x]
  
      X = []
      X.append(x)
  
      for i in range(1, self.num_branches):
          X.append(self.branches[i](args[i - 1]))
  
      x_fuse = []
      for i in range(len(self.fuse_layers)):
          y = X[0] if i == 0 else self.fuse_layers[i][0](X[0])
          for j in range(1, self.num_branches):
              if j > i:
                  if self.interp_type == "nearest":
                      y = F.broadcast_add(
                          y,
                          F.UpSampling(
                              self.fuse_layers[i][j](X[j]),
                              scale=2 ** (j - i),
                              sample_type="nearest",
                          ),
                      )
                  elif self.interp_type == "bilinear":
                      y = F.broadcast_add(
                          y,
                          F.contrib.BilinearResize2D(
                              self.fuse_layers[i][j](X[j]),
                              scale_height=2 ** (j - i),
                              scale_width=2 ** (j - i),
                              align_corners=False,
                          ),
                      )
                  elif self.interp_type == "bilinear_like":
                      y = F.broadcast_add(
                          y,
                          F.contrib.BilinearResize2D(
                              self.fuse_layers[i][j](X[j]),
                              like=X[i],
                              mode="like",
                              align_corners=False,
                          ),
                      )
                  else:
                      raise NotImplementedError
  
              else:
                  y = y + self.fuse_layers[i][j](X[j])
          x_fuse.append(F.relu(y))
  
      return x_fuse
  
imports: import numpy
  from mxnet.gluon import contrib
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.context import cpu
  from .resnet import BasicBlockV1, BottleneckV1, _conv3x3
label_desc: Use image node_id 6 for calling the HighResolutionModule obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x_fuse, list