code: def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
      r"""
      # type: (Tensor, float, float, float, float) -> Tensor
  
      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 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 6 for calling a global function with example usage: trunc_normal_(tensor, mean, std, a, b) and returns: _no_grad_trunc_normal_