code: def affect_conv_init(
      real_weight, imag_weight, kernel_size, init_func, criterion
  ):
      """Applies the weight initialization function given to the parameters.
      This is specifically written for convolutional layers.
  
      Arguments
      ---------
      real_weight: torch.Parameters
      imag_weight: torch.Parameters
      kernel_size: int
      init_func: function
          (unitary_init, complex_init)
      criterion: str
          (glorot, he)
      """
      in_channels = real_weight.size(1)
      out_channels = real_weight.size(0)
      a, b = init_func(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          criterion=criterion,
      )
      a, b = torch.from_numpy(a), torch.from_numpy(b)
      real_weight.data = a.type_as(real_weight.data)
      imag_weight.data = b.type_as(imag_weight.data)
  
file_path: speechbrain/speechbrain/nnet/complex_networks/c_ops.py
imports: import torch
  import torch.nn.functional
  import numpy
label_desc: Use image node_id 10 for calling a global function with example usage: affect_conv_init(real_weight, imag_weight, kernel_size, init_func, criterion) without return types