class_docstr: ## Down-sampling layer
docstr: :param x: is the input feature map with shape `[batch_size, channels, height, width]`
docstr_tok: [":","param","x",":","is","the","input","feature","map","with","shape","`","[","batch_size",",","channels",",","height",",","width","]","`"]
code: def forward(self, x: torch.Tensor):
      """
      :param x: is the input feature map with shape `[batch_size, channels, height, width]`
      """
      # Apply convolution
      return self.op(x)
  
imports: import math
  from typing import List
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from labml_nn.diffusion.stable_diffusion.model.unet_attention import SpatialTransformer
label_desc: Use image node_id 2 for calling the DownSample obj's underlying member method code with example usage: obj.forward(x) and returns: self