code: def quaternion_conv_op(
      input,
      r_weight,
      i_weight,
      j_weight,
      k_weight,
      bias,
      stride: int,
      padding: int,
      groups: int,
      dilation: int,
      conv1d: bool,
  ):
      """
      Applies a quaternion convolution transformation to the incoming data:
      It is important to notice that the forward phase of a QCNN is defined
      as W * Inputs (with * equal to the Hamilton product). The constructed
      cat_kernels_4_quaternion is a modified version of the quaternion
      representation so when we do torch.mm(Input,W) it's equivalent
      to W * Inputs.
  
      Arguments
      ---------
      input : torch.Tensor
          Quaternion input tensor to be transformed.
      conv1d : bool
          If true, a 1D convolution operation will be applied. Otherwise, a 2D
          convolution is called.
      r_weight : torch.Parameter
          Real part of the quaternion weight matrix of this layer.
      i_weight : torch.Parameter
          First imaginary part of the quaternion weight matrix of this layer.
      j_weight : torch.Parameter
          Second imaginary part of the quaternion weight matrix of this layer.
      k_weight : torch.Parameter
          Third imaginary part of the quaternion weight matrix of this layer.
      bias : torch.Parameter
      stride : int
          Stride factor of the convolutional filters.
      padding : int
          Amount of padding. See torch.nn documentation for more information.
      groups : int
          This option specifies the convolutional groups. See torch.nn
          documentation for more information.
      dilation : int
          Dilation factor of the convolutional filters.
      """
  
      cat_kernels_4_r = torch.cat(
          [r_weight, -i_weight, -j_weight, -k_weight], dim=1
      )
      cat_kernels_4_i = torch.cat(
          [i_weight, r_weight, -k_weight, j_weight], dim=1
      )
      cat_kernels_4_j = torch.cat(
          [j_weight, k_weight, r_weight, -i_weight], dim=1
      )
      cat_kernels_4_k = torch.cat(
          [k_weight, -j_weight, i_weight, r_weight], dim=1
      )
  
      cat_kernels_4_quaternion = torch.cat(
          [
              cat_kernels_4_r,
              cat_kernels_4_i,
              cat_kernels_4_j,
              cat_kernels_4_k,
          ],
          dim=0,
      )
  
      if conv1d:
          return F.conv1d(
              input=input,
              weight=cat_kernels_4_quaternion,
              bias=bias,
              stride=stride,
              padding=padding,
              dilation=dilation,
              groups=groups,
          )
      else:
          return F.conv2d(
              input=input,
              weight=cat_kernels_4_quaternion,
              bias=bias,
              stride=stride,
              padding=padding,
              dilation=dilation,
              groups=groups,
          )
  
file_path: speechbrain/speechbrain/nnet/quaternion_networks/q_ops.py
imports: import torch
  import math
  import numpy
  import torch.nn.functional
  from scipy.stats import chi
  from torch.autograd import Variable
label_desc: Use image node_id 4 for calling a global function with example usage: quaternion_conv_op(input, r_weight, i_weight, j_weight, k_weight, bias, stride, padding, groups, dilation, conv1d) and returns: F, F