code: def f(ws):
      """Transform 1.
  
      This method takes a complex Hermitian matrix represented by its
      upper triangular part and converts it to a block matrix
      representing the full original matrix with real numbers.
      The output tensor will have the following format:
      (*,2C,2C)
  
      Arguments
      ---------
      ws : tensor
          An input matrix. The tensor must have the following format:
          (*,2,C+P)
      """
  
      # Dimensions
      D = ws.dim()
      ws = ws.transpose(D - 2, D - 1)
      P = ws.shape[D - 2]
      C = int(round(((1 + 8 * P) ** 0.5 - 1) / 2))
  
      # Output matrix
      wsh = torch.zeros(
          ws.shape[0 : (D - 2)] + (2 * C, 2 * C),
          dtype=ws.dtype,
          device=ws.device,
      )
      ids = torch.triu_indices(C, C)
      wsh[..., ids[1] * 2, ids[0] * 2] = ws[..., 0]
      wsh[..., ids[0] * 2, ids[1] * 2] = ws[..., 0]
      wsh[..., ids[1] * 2 + 1, ids[0] * 2 + 1] = ws[..., 0]
      wsh[..., ids[0] * 2 + 1, ids[1] * 2 + 1] = ws[..., 0]
      wsh[..., ids[0] * 2, ids[1] * 2 + 1] = -1 * ws[..., 1]
      wsh[..., ids[1] * 2 + 1, ids[0] * 2] = -1 * ws[..., 1]
      wsh[..., ids[0] * 2 + 1, ids[1] * 2] = ws[..., 1]
      wsh[..., ids[1] * 2, ids[0] * 2 + 1] = ws[..., 1]
  
      return wsh
  
file_path: speechbrain/speechbrain/processing/decomposition.py
imports: import torch
label_desc: Use image node_id 3 for calling a global function with example usage: f(ws) and returns: wsh