code: def enhanceedges(
      image,
      mask=None,
      method="sobel",
      automatic_threshold=True,
      direction="all",
      automatic_gaussian=True,
      sigma=10,
      manual_threshold=0.2,
      threshold_adjustment_factor=1.0,
      automatic_low_threshold=True,
      low_threshold=0.1,
  ):
      """EnhanceEdges module
  
      Parameters
      ----------
      image : numpy.array
          Input image
      mask : numpy.array, optional
          Boolean mask, by default None
      method : str, optional
          Enhance edges algorithm to apply to the input image, by default "sobel"
      direction : str, optional
          Applicable to only the Sobel and Prewitt algorithms, by default "all"
      sigma : int, optional
          Applicable to only the Canny and Laplacian of Gaussian algorithms, by default 10. Only considered if automatic_gaussian is False.
      automatic_threshold : bool, optional
          Applicable only to the Canny algorithm, by default True
      manual_threshold : float, optional
          Applicable only to the Canny algorithm, by default 0.2
      threshold_adjustment_factor : float, optional
          Applicable only to the Canny algorithm, by default 1.0
      automatic_low_threshold : bool, optional
          Applicable only to the Canny algorithm, by default True
      low_threshold : float, optional
          Applicable only to the Canny algorithm, by default 0.1
  
      Returns
      -------
      numpy.array
          Image with enhanced edges
      """
  
      if not 0 <= low_threshold <= 1:
          warnings.warn(
              f"""low_threshold value of {low_threshold} is outside
              of the [0-1] CellProfiler default."""
          )
  
      if mask is None:
          mask = numpy.ones(image.shape, bool)
  
      if method.casefold() == "sobel":
          output_pixels = enhance_edges_sobel(image, mask, direction)
      elif method.casefold() == "log":
          output_pixels = enhance_edges_log(image, mask, sigma)
      elif method.casefold() == "prewitt":
          output_pixels = enhance_edges_prewitt(image, mask, direction)
      elif method.casefold() == "canny":
          output_pixels = enhance_edges_canny(
              image,
              mask,
              auto_threshold=automatic_threshold,
              auto_low_threshold=automatic_low_threshold,
              sigma=sigma,
              low_threshold=low_threshold,
              manual_threshold=manual_threshold,
              threshold_adjustment_factor=threshold_adjustment_factor,
          )
      elif method.casefold() == "roberts":
          output_pixels = centrosome.filter.roberts(image, mask)
      elif method.casefold() == "kirsch":
          output_pixels = centrosome.kirsch.kirsch(image)
      else:
          raise NotImplementedError(
              f"{method} edge detection method is not implemented."
          )
  
      return output_pixels
  
imports: import warnings
  import numpy
  import centrosome
  from ..functions.image_processing import enhance_edges_sobel, enhance_edges_log, enhance_edges_prewitt, enhance_edges_canny
label_desc: Use image node_id 1 for calling a global function with example usage: enhanceedges(image, mask, method, automatic_threshold, direction, automatic_gaussian, sigma, manual_threshold, threshold_adjustment_factor, automatic_low_threshold, low_threshold) and returns: output_pixels