code: def ginv(wsh):
      """Inverse transform 2.
  
      This method takes a complex Hermitian matrix represented by a block
      matrix and converts it to a full complex complex matrix. The
      result will have the following format:
      (*,C,C,2)
  
      Arguments
      ---------
      wsh : tensor
          An input matrix. The tensor must have the following format:
          (*,2C,2C)
      """
  
      # Extracting data
      D = wsh.dim()
      C = int(wsh.shape[D - 1] / 2)
  
      # Output matrix
      ws = torch.zeros(
          wsh.shape[0 : (D - 2)] + (C, C, 2),
          dtype=wsh.dtype,
          device=wsh.device,
      )
      ws[..., 0] = wsh[..., slice(0, 2 * C, 2), slice(0, 2 * C, 2)]
      ws[..., 1] = wsh[..., slice(1, 2 * C, 2), slice(0, 2 * C, 2)]
  
      return ws
  
file_path: speechbrain/speechbrain/processing/decomposition.py
imports: import torch
label_desc: Use image node_id 6 for calling a global function with example usage: ginv(wsh) and returns: ws