code: def tds_normal_(tensor, mode="fan_in"):
      """
      Normal Initialization from the paper [Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions](https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2460.pdf)
      Normalized to -
  
      .. math::
          \\text{bound} = \\text{2} \\times \\sqrt{\\frac{1}{\\text{fan\\_mode}}}
  
      Args:
          tensor: an n-dimensional `torch.Tensor`
          mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
              preserves the magnitude of the variance of the weights in the
              forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
              backwards pass.
      """
      fan = _calculate_correct_fan(tensor, mode)
      gain = 2.0
      std = gain / math.sqrt(fan)  # sqrt(4.0 / fan_in)
      bound = std  # Calculate uniform bounds from standard deviation
      with torch.no_grad():
          return tensor.normal_(0.0, bound)
  
imports: import math
  from typing import Callable, Iterable, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.init import _calculate_correct_fan
  from torch.nn.modules.utils import _single
  from nemo.collections.common.parts.utils import activation_registry
  from nemo.core.classes.mixins import AccessMixin
  from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: tds_normal_(tensor, mode) and returns: tensor