class_docstr: Calculates an adversarial loss on a Patch Discriminator or a Multi-scale Patch Discriminator.
  Warning: due to the possibility of using different criterions, the output of the discrimination
  mustn't be passed to a final activation layer. That is taken care of internally within the loss.
  
  Args:
      reduction: {``"none"``, ``"mean"``, ``"sum"``}
          Specifies the reduction to apply to the output. Defaults to ``"mean"``.
  
          - ``"none"``: no reduction will be applied.
          - ``"mean"``: the sum of the output will be divided by the number of elements in the output.
          - ``"sum"``: the output will be summed.
  
      criterion: which criterion (hinge, least_squares or bce) you want to use on the discriminators outputs.
          Depending on the criterion, a different activation layer will be used. Make sure you don't run the outputs
          through an activation layer prior to calling the loss.
      no_activation_leastsq: if True, the activation layer in the case of least-squares is removed.
docstr: Gets a zero tensor.
  
  Args:
      input: tensor which shape you want the zeros tensor to correspond to.
  Returns:
docstr_tok: ["Gets","a","zero","tensor",".","Args",":","input",":","tensor","which","shape","you","want","the","zeros","tensor","to","correspond","to",".","Returns",":"]
code: def get_zero_tensor(self, input: torch.Tensor) -> torch.Tensor:
      """
      Gets a zero tensor.
  
      Args:
          input: tensor which shape you want the zeros tensor to correspond to.
      Returns:
      """
  
      zero_label_tensor = (
          torch.tensor(0).type(input[0].type()).to(input[0].device)
      )
      zero_label_tensor.requires_grad_(False)
      return zero_label_tensor.expand_as(input)
  
file_path: MONAI/monai/losses/adversarial_loss.py
imports: from __future__ import annotations
  import warnings
  import torch
  from torch.nn.modules.loss import _Loss
  from monai.networks.layers.utils import get_act_layer
  from monai.utils import LossReduction
  from monai.utils.enums import StrEnum
label_desc: Use image node_id 3 for calling the PatchAdversarialLoss obj's underlying member method code with example usage: obj.get_zero_tensor(input) and returns: zero_label_tensor