code: def _rdft_matrix(dft_length):
      """Return a precalculated DFT matrix for positive frequencies only
  
      Args:
          dft_length (int) - DFT length
  
      Returns
          rdft_mat (k x n tensor) - precalculated DFT matrix rows are frequencies
              columns are samples, k dimension is dft_length // 2 +1 bins long
      """
      # freq bins
      k = np.arange(0, dft_length // 2 + 1)
      # Samples
      n = np.arange(0, dft_length)
      # complex frequency vector (now normalised to 2 pi)
      omega = -1j * 2.0 * np.pi / dft_length * k
      # complex phase, compute a matrix of value for the complex phase for each sample
      # location (n) and each freq bin (k) outer product If the two vectors have dimensions
      # k and n, then their outer product is an k × n matrix
      phase = np.outer(omega, n)
      # return transposed ready for matrix multiplication
      return np.exp(phase).astype(np.complex64).T
  
file_path: kapre/kapre/tflite_compatible_stft.py
imports: import math
  import tensorflow
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: _rdft_matrix(dft_length) and returns: np