code: def _no_grad_trunc_normal_(tensor, mean, std, a, b):
      # 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
          l = norm_cdf((a - mean) / std)
          u = norm_cdf((b - mean) / std)
  
          # Uniformly fill tensor with values from [l, u], then translate to
          # [2l-1, 2u-1].
          tensor.uniform_(2 * l - 1, 2 * u - 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 logging
  import math
  import warnings
  from enum import Enum
  from typing import Dict, List, Tuple
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.utils import _ntuple
  from torch.utils.checkpoint import checkpoint
  from vissl.data.collators.collator_helper import MultiDimensionalTensor
  from vissl.utils.activation_checkpointing import checkpoint_trunk
  from vissl.utils.env import get_machine_local_and_dist_rank
  from vissl.utils.misc import is_apex_available
label_desc: Use image node_id 16 for calling a global function with example usage: _no_grad_trunc_normal_(tensor, mean, std, a, b) and returns: unknown, tensor