diff --git a/timm/models/layers/blurpool.py b/timm/models/layers/blurpool.py
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+'''independent attempt to implement
+
+MaxBlurPool2d in a more general fashion(separate maxpooling from BlurPool)
+which was again inspired by
+Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
+
+'''
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class BlurPool2d(nn.Module):
+    r"""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
+        blur_filter_size (int): filter size for blurring. currently supports either 3 or 5 (most common)
+                                defaults to 3.
+        stride (int): downsampling filter stride
+    Shape:
+    Returns:
+        torch.Tensor: the transformed tensor.
+    Examples:
+    """
+
+    def __init__(self, channels=None, blur_filter_size=3, stride=2) -> None:
+        super(BlurPool2d, self).__init__()
+        assert blur_filter_size in [3, 5]
+        self.channels = channels
+        self.blur_filter_size = blur_filter_size
+        self.stride = stride
+
+        if blur_filter_size == 3:
+            pad_size = [1] * 4
+            blur_matrix = torch.Tensor([[1., 2., 1]]) / 4 # binomial kernel b2
+        else:
+            pad_size = [2] * 4
+            blur_matrix = torch.Tensor([[1., 4., 6., 4., 1.]]) / 16 # binomial filter kernel b4
+
+        self.padding = nn.ReflectionPad2d(pad_size)
+        blur_filter = blur_matrix * blur_matrix.T
+        self.register_buffer('blur_filter', blur_filter[None, None, :, :].repeat((self.channels, 1, 1, 1)))
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: # type: ignore
+        if not torch.is_tensor(input_tensor):
+            raise TypeError("Input input type is not a torch.Tensor. Got {}"
+                            .format(type(input_tensor)))
+        if not len(input_tensor.shape) == 4:
+            raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}"
+                             .format(input_tensor.shape))
+        # apply blur_filter on input
+        return F.conv2d(self.padding(input_tensor), self.blur_filter, stride=self.stride, groups=input_tensor.shape[1])
+
+
+######################
+# functional interface
+######################
+
+
+'''def blur_pool2d() -> torch.Tensor:
+    r"""Creates a module that computes pools and blurs and downsample a given
+    feature map.
+    See :class:`~kornia.contrib.MaxBlurPool2d` for details.
+    """
+    return BlurPool2d(kernel_size, ceil_mode)(input)'''
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