code: def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
      r"""Supposedly should be available in PyTorch soon. Replace when available.
      Fills the input Tensor with values drawn
      from a truncated normal distribution. The values are effectively drawn from the
      normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
      with values outside :math:`[a, b]` redrawn until they are within
      the bounds. The method used for generating the random values works
      best when :math:`a \leq \text{mean} \leq b`.
  
      Args:
          tensor: an n-dimensional `torch.Tensor`
          mean: the mean of the normal distribution
          std: the standard deviation of the normal distribution
          a: the minimum cutoff value
          b: the maximum cutoff value
  
      Examples:
          >>> w = torch.empty(3, 5)
          >>> nn.init.trunc_normal_(w)
      """
      return _no_grad_trunc_normal_(tensor, mean, std, a, b)
  
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 15 for calling a global function with example usage: trunc_normal_(tensor, mean, std, a, b) and returns: _no_grad_trunc_normal_