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: 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 math
  import warnings
  from collections import OrderedDict
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  from torch import nn
  from lavis.models.clip_models.utils import freeze_batch_norm_2d
label_desc: Use image node_id 11 for calling a global function with example usage: trunc_normal_(tensor, mean, std, a, b) and returns: _no_grad_trunc_normal_