code: def quaternion_linear_op(
      input, r_weight, i_weight, j_weight, k_weight, bias
  ):
      """
      Applies a quaternion linear transformation to the incoming data:
      It is important to notice that the forward phase of a QNN 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.
      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
      """
  
      cat_kernels_4_r = torch.cat(
          [r_weight, -i_weight, -j_weight, -k_weight], dim=0
      )
      cat_kernels_4_i = torch.cat(
          [i_weight, r_weight, -k_weight, j_weight], dim=0
      )
      cat_kernels_4_j = torch.cat(
          [j_weight, k_weight, r_weight, -i_weight], dim=0
      )
      cat_kernels_4_k = torch.cat(
          [k_weight, -j_weight, i_weight, r_weight], dim=0
      )
      cat_kernels_4_quaternion = torch.cat(
          [
              cat_kernels_4_r,
              cat_kernels_4_i,
              cat_kernels_4_j,
              cat_kernels_4_k,
          ],
          dim=1,
      )
  
      # If the input is already [batch*time, N]
      if input.dim() == 2:
          if bias.requires_grad:
              return torch.addmm(bias, input, cat_kernels_4_quaternion)
          else:
              return torch.mm(input, cat_kernels_4_quaternion)
      else:
          output = torch.matmul(input, cat_kernels_4_quaternion)
          if bias.requires_grad:
              return output + bias
          else:
              return output
  
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 1 for calling a global function with example usage: quaternion_linear_op(input, r_weight, i_weight, j_weight, k_weight, bias) and returns: torch, torch, unknown, output