code: def pink_noise_like(
      waveforms, alpha_low=1.0, alpha_high=1.0, sample_rate=50
  ):
      """Creates a sequence of pink noise (also known as 1/f). The pink noise
      is obtained by multipling the spectrum of a white noise sequence by a
      factor (1/f^alpha).
      The alpha factor controls the decrease factor in the frequnecy domain
      (alpha=0 adds white noise, alpha>>0 adds low frequnecy noise). It is
      randomly sampled between alpha_low and alpha_high. With negative alpha this
      funtion generates blue noise.
  
      Arguments
      ---------
      waveforms : torch.Tensor
          The original waveform. It is just used to infer the shape.
      alpha_low : float
          The minimum value for the alpha spectral smooting factor.
      alpha_high : float
          The maximum value for the alpha spectral smooting factor.
      sample_rate : float
          The sample rate of the original signal.
  
      Example
      -------
      >>> waveforms = torch.randn(4,257,10)
      >>> noise = pink_noise_like(waveforms)
      >>> noise.shape
      torch.Size([4, 257, 10])
      """
      # Sampling white noise (flat spectrum)
      white_noise = torch.randn_like(waveforms)
  
      # Computing the fft of the input white noise
      white_noise_fft = torch.fft.fft(white_noise, dim=1)
  
      # Sampling the spectral smoothing factor
      rand_range = alpha_high - alpha_low
      alpha = (
          torch.rand(waveforms.shape[0], device=waveforms.device)
          * rand_range
          + alpha_low
      )
  
      # preparing the spectral mask (1/f^alpha)
      f = torch.linspace(
          0,
          sample_rate / 2,
          int(white_noise.shape[1] / 2),
          device=waveforms.device,
      )
      spectral_mask = 1 / torch.pow(f.unsqueeze(0), alpha.unsqueeze(1))
  
      # Avoid inf due to 1/0 division at f=0
      spectral_mask[:, 0] = spectral_mask[:, 1]
  
      # Mask for the upper part of the spectrum (f > sample_rate/2)
      spectral_mask_up = torch.flip(spectral_mask, dims=(1,))
  
      # Managing odd/even sequences
      if white_noise.shape[1] % 2:
          mid_element = spectral_mask[
              :, int(white_noise.shape[1] / 2) - 1
          ].unsqueeze(1)
          spectral_mask = torch.cat(
              [spectral_mask, mid_element, spectral_mask_up], dim=1
          )
      else:
          spectral_mask = torch.cat(
              [spectral_mask, spectral_mask_up], dim=1
          )
  
      # Managing multi-channel inputs
      if len(white_noise.shape) == 3:
          spectral_mask = spectral_mask.unsqueeze(2)
  
      # Spectral masking
      pink_noise_fft = white_noise_fft * spectral_mask
  
      # Return to the time-domain
      pink_noise = torch.fft.ifft(pink_noise_fft, dim=1).real
      return pink_noise
  
imports: import math
  import random
  import torch
  import torch.nn.functional
  from speechbrain.dataio.legacy import ExtendedCSVDataset
  from speechbrain.dataio.dataloader import make_dataloader
  from speechbrain.processing.signal_processing import compute_amplitude, dB_to_amplitude, convolve1d, notch_filter, reverberate
label_desc: Use image node_id 1 for calling a global function with example usage: pink_noise_like(waveforms, alpha_low, alpha_high, sample_rate) and returns: pink_noise