code: def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
      r"""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 (``torch.Tensor``): an n-dimensional `torch.Tensor`
          mean (float): the mean of the normal distribution
          std (float): the standard deviation of the normal distribution
          a (float): the minimum cutoff value
          b (float): the maximum cutoff value
      """
      return _no_grad_trunc_normal_(tensor, mean, std, a, b)
  
imports: import math
  import warnings
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: trunc_normal_(tensor, mean, std, a, b) and returns: _no_grad_trunc_normal_