code: def _uniform_filter(inputs: Tensor, window_size: int) -> Tensor:
      """Apply uniform filter with a window of a given size over the input image.
  
      Args:
          inputs: Input image
          window_size: Sliding window used for rmse calculation
  
      Return:
          Image transformed with the uniform input
  
      """
      inputs = _reflection_pad_2d(
          inputs, window_size // 2, window_size % 2
      )
      kernel_weight, kernel_bias = _uniform_weight_bias_conv2d(
          inputs, window_size
      )
      # Iterate over channels
      return torch.cat(
          [
              F.conv2d(
                  inputs[:, channel].unsqueeze(1),
                  kernel_weight,
                  kernel_bias,
                  padding=0,
              )
              for channel in range(inputs.shape[1])
          ],
          dim=1,
      )
  
file_path: torchmetrics/src/torchmetrics/functional/image/helper.py
imports: from typing import Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn import functional
label_desc: Use image node_id 6 for calling a global function with example usage: _uniform_filter(inputs, window_size) and returns: torch