code: def xavier_uniform_(
      tensor: Tensor,
      gain: float = 1.0,
      generator: _Optional[torch.Generator] = None,
  ) -> Tensor:
      r"""Fill the input `Tensor` with values using a Xavier uniform distribution.
  
      The method is described in `Understanding the difficulty of training
      deep feedforward neural networks` - Glorot, X. & Bengio, Y. (2010).
      The resulting tensor will have values sampled from
      :math:`\mathcal{U}(-a, a)` where
  
      .. math::
          a = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}}
  
      Also known as Glorot initialization.
  
      Args:
          tensor: an n-dimensional `torch.Tensor`
          gain: an optional scaling factor
          generator: the torch Generator to sample from (default: None)
  
      Examples:
          >>> w = torch.empty(3, 5)
          >>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu'))
      """
      fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
      std = gain * math.sqrt(2.0 / float(fan_in + fan_out))
      a = (
          math.sqrt(3.0) * std
      )  # Calculate uniform bounds from standard deviation
  
      return _no_grad_uniform_(tensor, -a, a, generator)
  
imports: import math
  import warnings
  from torch import Tensor
  import torch
  from typing import Optional
label_desc: Use image node_id 16 for calling a global function with example usage: xavier_uniform_(tensor, gain, generator) and returns: _no_grad_uniform_