code: def exp_sigmoid(
      input: torch.Tensor,
      exponent: float = 10.0,
      max_value: float = 2.0,
      threshold: float = 1e-7,
  ) -> torch.Tensor:
      """Exponential Sigmoid pointwise nonlinearity.
      Implements the equation:
      ``max_value`` * sigmoid(``input``) ** (log(``exponent``)) + ``threshold``
  
      The output has a range of [``threshold``, ``max_value``].
      ``exponent`` controls the slope of the output.
  
      .. devices:: CPU CUDA
  
      Args:
          input (Tensor): Input Tensor
          exponent (float, optional): Exponent. Controls the slope of the output
          max_value (float, optional): Maximum value of the output
          threshold (float, optional): Minimum value of the output
  
      Returns:
          Tensor: Exponential Sigmoid output. Shape: same as input
  
      """
  
      return max_value * torch.pow(
          torch.nn.functional.sigmoid(input),
          torch.log(
              torch.tensor(
                  exponent, device=input.device, dtype=input.dtype
              )
          ),
      ) + torch.tensor(
          threshold, device=input.device, dtype=input.dtype
      )
  
file_path: audio/src/torchaudio/prototype/functional/_dsp.py
imports: import warnings
  from typing import List, Optional, Union
  import torch
  from torchaudio.functional import fftconvolve
label_desc: Use image node_id 8 for calling a global function with example usage: exp_sigmoid(input, exponent, max_value, threshold) and returns: unknown