code: def get_statistics_with_mask(
      x: torch.Tensor, m: torch.Tensor, dim: int = 2, eps: float = 1e-10
  ):
      """
      compute mean and standard deviation of input(x) provided with its masking labels (m)
      input:
          x: feature input
          m: averaged mask labels
      output:
          mean: mean of input features
          std: stadard deviation of input features
      """
      mean = torch.sum((m * x), dim=dim)
      std = torch.sqrt(
          (m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(eps)
      )
      return mean, std
  
imports: from typing import List
  import torch
  from numpy import inf
  from torch import nn
  from torch.nn import functional
  from nemo.collections.asr.parts.submodules.jasper import get_same_padding, init_weights
label_desc: Use image node_id 3 for calling a global function with example usage: get_statistics_with_mask(x, m, dim, eps) and returns: mean,  std