code: def forward(self, x):
      batchsize, D, N = x.size()
      if self.use_bn:
          x = torch.max(x, 2, keepdim=True)[0] + offset_map[N]
      else:
          x = torch.max(x, 2, keepdim=True)[0] - 0.3
      return x
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from basic import BiLinear
  from torch.autograd import Variable
label_desc: Use image node_id 2 for calling the EmaMaxPool obj's underlying member method code with example usage: obj.forward(x) and returns: x