code: def _no_grad_trunc_normal_(tensor, mean, std, a, b):
      # Cut & paste from PyTorch official master until it's in more releases - RW
      # Method based on
      # https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
      def norm_cdf(x):
          # Computes standard normal cumulative distribution function
          return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
  
      if (mean < a - 2 * std) or (mean > b + 2 * std):
          warnings.warn(
              "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
              "The distribution of values may be incorrect.",
              stacklevel=2,
          )
  
      with torch.no_grad():
          # Values are generated by using a truncated uniform distribution and
          # then using the inverse CDF for the normal distribution.
          # Get upper and lower cdf values
          lower = norm_cdf((a - mean) / std)
          upper = norm_cdf((b - mean) / std)
  
          # Uniformly fill tensor with values from [l, u], then translate to
          # [2l-1, 2u-1].
          tensor.uniform_(2 * lower - 1, 2 * upper - 1)
  
          # Use inverse cdf transform for normal distribution to get truncated
          # standard normal
          tensor.erfinv_()
  
          # Transform to proper mean, std
          tensor.mul_(std * math.sqrt(2.0))
          tensor.add_(mean)
  
          # Clamp to ensure it's in the proper range
          tensor.clamp_(min=a, max=b)
          return tensor
  
imports: import collections.abc
  import math
  import warnings
  from dataclasses import dataclass
  from functools import partial
  from itertools import repeat
  import omegaconf
  import torch
  import torch.nn
  import torch.nn.functional
  from mmf.common.registry import registry
  from mmf.modules.encoders import Encoder
label_desc: Use image node_id 5 for calling a global function with example usage: _no_grad_trunc_normal_(tensor, mean, std, a, b) and returns: unknown, tensor