code: def _uniform_weight_bias_conv2d(
      inputs: Tensor, window_size: int
  ) -> Tuple[Tensor, Tensor]:
      """Construct uniform weight and bias for a 2d convolution.
  
      Args:
          inputs: Input image
          window_size: size of convolutional kernel
  
      Return:
          The weight and bias for 2d convolution
  
      """
      kernel_weight = torch.ones(
          1,
          1,
          window_size,
          window_size,
          dtype=inputs.dtype,
          device=inputs.device,
      )
      kernel_weight /= window_size**2
      kernel_bias = torch.zeros(
          1, dtype=inputs.dtype, device=inputs.device
      )
      return kernel_weight, kernel_bias
  
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 3 for calling a global function with example usage: _uniform_weight_bias_conv2d(inputs, window_size) and returns: kernel_weight,  kernel_bias