code: def flat_subsample(self, tensor):
      size = tensor.size()
      if len(size) >= 2:
          batch_size = size[0] * (size[1] // 2)
          expanded_size = (
              (batch_size, 2) + size[2:]
              if len(size) > 2
              else (batch_size, 2)
          )
          tensor = tensor.view(expanded_size)
          tensor = torch.cat([tensor[:, 0], tensor[:, 1]], dim=0)
      return tensor
  
imports: import torch
  from .task import Task
label_desc: Use image node_id 1 for calling the VLMTask obj's underlying member method code with example usage: obj.flat_subsample(tensor) and returns: tensor