code: def spectrogram(
      waveform: Tensor,
      blackman_coeff: float = 0.42,
      channel: int = -1,
      dither: float = 0.0,
      energy_floor: float = 1.0,
      frame_length: float = 25.0,
      frame_shift: float = 10.0,
      min_duration: float = 0.0,
      preemphasis_coefficient: float = 0.97,
      raw_energy: bool = True,
      remove_dc_offset: bool = True,
      round_to_power_of_two: bool = True,
      sample_frequency: float = 16000.0,
      snip_edges: bool = True,
      subtract_mean: bool = False,
      window_type: str = POVEY,
  ) -> Tensor:
      r"""Create a spectrogram from a raw audio signal. This matches the input/output of Kaldi's
      compute-spectrogram-feats.
  
      Args:
          waveform (Tensor): Tensor of audio of size (c, n) where c is in the range [0,2)
          blackman_coeff (float, optional): Constant coefficient for generalized Blackman window. (Default: ``0.42``)
          channel (int, optional): Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (Default: ``-1``)
          dither (float, optional): Dithering constant (0.0 means no dither). If you turn this off, you should set
              the energy_floor option, e.g. to 1.0 or 0.1 (Default: ``0.0``)
          energy_floor (float, optional): Floor on energy (absolute, not relative) in Spectrogram computation.  Caution:
              this floor is applied to the zeroth component, representing the total signal energy.  The floor on the
              individual spectrogram elements is fixed at std::numeric_limits<float>::epsilon(). (Default: ``1.0``)
          frame_length (float, optional): Frame length in milliseconds (Default: ``25.0``)
          frame_shift (float, optional): Frame shift in milliseconds (Default: ``10.0``)
          min_duration (float, optional): Minimum duration of segments to process (in seconds). (Default: ``0.0``)
          preemphasis_coefficient (float, optional): Coefficient for use in signal preemphasis (Default: ``0.97``)
          raw_energy (bool, optional): If True, compute energy before preemphasis and windowing (Default: ``True``)
          remove_dc_offset (bool, optional): Subtract mean from waveform on each frame (Default: ``True``)
          round_to_power_of_two (bool, optional): If True, round window size to power of two by zero-padding input
              to FFT. (Default: ``True``)
          sample_frequency (float, optional): Waveform data sample frequency (must match the waveform file, if
              specified there) (Default: ``16000.0``)
          snip_edges (bool, optional): If True, end effects will be handled by outputting only frames that completely fit
              in the file, and the number of frames depends on the frame_length.  If False, the number of frames
              depends only on the frame_shift, and we reflect the data at the ends. (Default: ``True``)
          subtract_mean (bool, optional): Subtract mean of each feature file [CMS]; not recommended to do
              it this way.  (Default: ``False``)
          window_type (str, optional): Type of window ('hamming'|'hanning'|'povey'|'rectangular'|'blackman')
           (Default: ``'povey'``)
  
      Returns:
          Tensor: A spectrogram identical to what Kaldi would output. The shape is
          (m, ``padded_window_size // 2 + 1``) where m is calculated in _get_strided
      """
      device, dtype = waveform.device, waveform.dtype
      epsilon = _get_epsilon(device, dtype)
  
      (
          waveform,
          window_shift,
          window_size,
          padded_window_size,
      ) = _get_waveform_and_window_properties(
          waveform,
          channel,
          sample_frequency,
          frame_shift,
          frame_length,
          round_to_power_of_two,
          preemphasis_coefficient,
      )
  
      if len(waveform) < min_duration * sample_frequency:
          # signal is too short
          return torch.empty(0)
  
      strided_input, signal_log_energy = _get_window(
          waveform,
          padded_window_size,
          window_size,
          window_shift,
          window_type,
          blackman_coeff,
          snip_edges,
          raw_energy,
          energy_floor,
          dither,
          remove_dc_offset,
          preemphasis_coefficient,
      )
  
      # size (m, padded_window_size // 2 + 1, 2)
      fft = torch.fft.rfft(strided_input)
  
      # Convert the FFT into a power spectrum
      power_spectrum = torch.max(
          fft.abs().pow(2.0), epsilon
      ).log()  # size (m, padded_window_size // 2 + 1)
      power_spectrum[:, 0] = signal_log_energy
  
      power_spectrum = _subtract_column_mean(
          power_spectrum, subtract_mean
      )
      return power_spectrum
  
imports: import math
  from typing import Tuple
  import torch
  import torchaudio
  from torch import Tensor
label_desc: Use image node_id 9 for calling a global function with example usage: spectrogram(waveform, blackman_coeff, channel, dither, energy_floor, frame_length, frame_shift, min_duration, preemphasis_coefficient, raw_energy, remove_dc_offset, round_to_power_of_two, sample_frequency, snip_edges, subtract_mean, window_type) and returns: power_spectrum, torch