class_docstr: Creates a module that computes blurs and downsample a given feature map.
  See :cite:`zhang2019shiftinvar` for more details.
  Corresponds to the Downsample class, which does blurring and subsampling
  
  Args:
      channels = Number of input channels
      filt_size (int): binomial filter size for blurring. currently supports 3 (default) and 5.
      stride (int): downsampling filter stride
  
  Returns:
      torch.Tensor: the transformed tensor.
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      x = F.pad(x, self.padding, "reflect")
      return F.conv2d(
          x, self.filt, stride=self.stride, groups=self.channels
      )
  
file_path: pytorch-image-models/timm/layers/blur_pool.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from .padding import get_padding
label_desc: Use image node_id 2 for calling the BlurPool2d obj's underlying member method code with example usage: obj.forward(x) and returns: F