code: def trunc_normal_(
      tensor: Tensor,
      mean: float = 0.0,
      std: float = 1.0,
      a: float = -2.0,
      b: float = 2.0,
  ) -> Tensor:
      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`.
  
      Modified from
      https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
  
      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 copy
  import math
  import warnings
  import numpy
  import torch
  import torch.nn
  from torch import Tensor
  from annotator.uniformer.mmcv.utils import Registry, build_from_cfg, get_logger, print_log
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_