code: def mel_weight_matrix(
      num_mel_bins: int,
      dft_length: int,
      sample_rate: int,
      lower_edge_hertz: float = 125.0,
      upper_edge_hertz: float = 3000.0,
  ):
      # transform the inputs to tensors
      lower_edge_hertz = torch.tensor(lower_edge_hertz)
      upper_edge_hertz = torch.tensor(upper_edge_hertz)
      zero = torch.tensor(0.0)
  
      # mel transform lambda function
      def hz_to_mel(f):
          return 2595 * torch.log10(1 + f / 700)
  
      nyquist_hz = sample_rate / 2
      # define a range of frequencies in HZ
      linear_freqs = torch.linspace(0, nyquist_hz, dft_length)[1:]
      # transform the frequencies from HZ to mels
      spec_bin_mels = hz_to_mel(linear_freqs).unsqueeze(1)
      mel_edges = torch.linspace(
          hz_to_mel(lower_edge_hertz),
          hz_to_mel(upper_edge_hertz),
          num_mel_bins + 2,
      )
      # create overlapping frames of size 3
      mel_edges = mel_edges.unfold(0, size=3, step=1)
      lower_edge_mel, center_mel, upper_edge_mel = (
          t.reshape((1, num_mel_bins))
          for t in mel_edges.split(1, dim=1)
      )
      lower_slopes = (spec_bin_mels - lower_edge_mel) / (
          center_mel - lower_edge_mel
      )
      upper_slopes = (upper_edge_mel - spec_bin_mels) / (
          upper_edge_mel - center_mel
      )
      mel_weights = torch.maximum(
          zero, torch.minimum(lower_slopes, upper_slopes)
      )
      return torch.nn.functional.pad(mel_weights, (0, 0, 1, 0))
  
file_path: ivy/ivy/functional/backends/torch/experimental/creation.py
imports: from typing import Optional, Tuple, Union
  import math
  import torch
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_unsupported_device_and_dtypes
  from ..None import backend_version
label_desc: Use image node_id 10 for calling a global function with example usage: mel_weight_matrix(num_mel_bins, dft_length, sample_rate, lower_edge_hertz, upper_edge_hertz) and returns: torch, unknown