content: import torch.nn as nn
  from ..modules import sparse as sp
  
  FP16_MODULES = (
      nn.Conv1d,
      nn.Conv2d,
      nn.Conv3d,
      nn.ConvTranspose1d,
      nn.ConvTranspose2d,
      nn.ConvTranspose3d,
      nn.Linear,
      sp.SparseConv3d,
      sp.SparseInverseConv3d,
      sp.SparseLinear,
  )
  
  def convert_module_to_f16(l):
      """
      Convert primitive modules to float16.
      """
      if isinstance(l, FP16_MODULES):
          for p in l.parameters():
              p.data = p.data.half()
  
  
  def convert_module_to_f32(l):
      """
      Convert primitive modules to float32, undoing convert_module_to_f16().
      """
      if isinstance(l, FP16_MODULES):
          for p in l.parameters():
              p.data = p.data.float()
  
  
  def zero_module(module):
      """
      Zero out the parameters of a module and return it.
      """
      for p in module.parameters():
          p.detach().zero_()
      return module
  
  
  def scale_module(module, scale):
      """
      Scale the parameters of a module and return it.
      """
      for p in module.parameters():
          p.detach().mul_(scale)
      return module
  
  
  def modulate(x, shift, scale):
      return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
  