parameters: (self, images, style=None, verbose=True, max_size=800, batch_size=4)
docstring: Stylize an SFrame of Images given a style index or a list of
          styles.
  
          Parameters
          ----------
          images : SFrame | SArray | turicreate.Image
              A dataset that has the same content image column that was used
              during training.
  
          style : None | int | list
              The selected style or list of styles to use on the ``images``. If
              `None`, all styles will be applied to each image in ``images``.
  
          verbose : bool, optional
              If True, print progress updates.
  
          max_size : int or tuple
              Max input image size that will not get resized during stylization.
  
              Images with a side larger than this value, will be scaled down, due
              to time and memory constraints. If tuple, interpreted as (max
              width, max height). Without resizing, larger input images take more
              time to stylize.  Resizing can effect the quality of the final
              stylized image.
  
          batch_size : int, optional
              If you are getting memory errors, try decreasing this value. If you
              have a powerful computer, increasing this value may improve
              performance.
  
          Returns
          -------
          out : SFrame or SArray or turicreate.Image
              If ``style`` is a list, an SFrame is always returned. If ``style``
              is a single integer, the output type will match the input type
              (Image, SArray, or SFrame).
  
          See Also
          --------
          create
  
          Examples
          --------
          >>> image = tc.Image("/path/to/image.jpg")
          >>> stylized_images = model.stylize(image, style=[0, 1])
          Data:
          +--------+-------+------------------------+
          | row_id | style |     stylized_image     |
          +--------+-------+------------------------+
          |   0    |   0   | Height: 256 Width: 256 |
          |   0    |   1   | Height: 256 Width: 256 |
          +--------+-------+------------------------+
          [2 rows x 3 columns]
  
          >>> images = tc.image_analysis.load_images('/path/to/images')
          >>> stylized_images = model.stylize(images)
          Data:
          +--------+-------+------------------------+
          | row_id | style |     stylized_image     |
          +--------+-------+------------------------+
          |   0    |   0   | Height: 256 Width: 256 |
          |   0    |   1   | Height: 256 Width: 256 |
          |   0    |   2   | Height: 256 Width: 256 |
          |   0    |   3   | Height: 256 Width: 256 |
          |   1    |   0   | Height: 640 Width: 648 |
          |   1    |   1   | Height: 640 Width: 648 |
          |   1    |   2   | Height: 640 Width: 648 |
          |   1    |   3   | Height: 640 Width: 648 |
          +--------+-------+------------------------+
          [8 rows x 3 columns]
docstring_summary: Stylize an SFrame of Images given a style index or a list of
          styles.
docstring_tokens: Stylize an SFrame of Images given a style index or a list of styles .
function: def stylize(self, images, style=None, verbose=True, max_size=800, batch_size=4):
          """
          Stylize an SFrame of Images given a style index or a list of
          styles.
  
          Parameters
          ----------
          images : SFrame | SArray | turicreate.Image
              A dataset that has the same content image column that was used
              during training.
  
          style : None | int | list
              The selected style or list of styles to use on the ``images``. If
              `None`, all styles will be applied to each image in ``images``.
  
          verbose : bool, optional
              If True, print progress updates.
  
          max_size : int or tuple
              Max input image size that will not get resized during stylization.
  
              Images with a side larger than this value, will be scaled down, due
              to time and memory constraints. If tuple, interpreted as (max
              width, max height). Without resizing, larger input images take more
              time to stylize.  Resizing can effect the quality of the final
              stylized image.
  
          batch_size : int, optional
              If you are getting memory errors, try decreasing this value. If you
              have a powerful computer, increasing this value may improve
              performance.
  
          Returns
          -------
          out : SFrame or SArray or turicreate.Image
              If ``style`` is a list, an SFrame is always returned. If ``style``
              is a single integer, the output type will match the input type
              (Image, SArray, or SFrame).
  
          See Also
          --------
          create
  
          Examples
          --------
          >>> image = tc.Image("/path/to/image.jpg")
          >>> stylized_images = model.stylize(image, style=[0, 1])
          Data:
          +--------+-------+------------------------+
          | row_id | style |     stylized_image     |
          +--------+-------+------------------------+
          |   0    |   0   | Height: 256 Width: 256 |
          |   0    |   1   | Height: 256 Width: 256 |
          +--------+-------+------------------------+
          [2 rows x 3 columns]
  
          >>> images = tc.image_analysis.load_images('/path/to/images')
          >>> stylized_images = model.stylize(images)
          Data:
          +--------+-------+------------------------+
          | row_id | style |     stylized_image     |
          +--------+-------+------------------------+
          |   0    |   0   | Height: 256 Width: 256 |
          |   0    |   1   | Height: 256 Width: 256 |
          |   0    |   2   | Height: 256 Width: 256 |
          |   0    |   3   | Height: 256 Width: 256 |
          |   1    |   0   | Height: 640 Width: 648 |
          |   1    |   1   | Height: 640 Width: 648 |
          |   1    |   2   | Height: 640 Width: 648 |
          |   1    |   3   | Height: 640 Width: 648 |
          +--------+-------+------------------------+
          [8 rows x 3 columns]
          """
          if not isinstance(images, (_tc.SFrame, _tc.SArray, _tc.Image)):
              raise TypeError(
                  '"image" parameter must be of type SFrame, SArray or turicreate.Image.'
              )
          if isinstance(images, (_tc.SFrame, _tc.SArray)) and len(images) == 0:
              raise _ToolkitError('"image" parameter cannot be empty')
          if style is not None and not isinstance(style, (int, list)):
              raise TypeError('"style" must parameter must be a None, int or a list')
          if not isinstance(max_size, int):
              raise TypeError('"max_size" must parameter must be an int')
          if max_size < 1:
              raise _ToolkitError("'max_size' must be greater than or equal to 1")
          if not isinstance(batch_size, int):
              raise TypeError('"batch_size" must parameter must be an int')
          if batch_size < 1:
              raise _ToolkitError("'batch_size' must be greater than or equal to 1")
  
          options = {}
          options["style_idx"] = style
          options["verbose"] = verbose
          options["max_size"] = max_size
          options["batch_size"] = batch_size
  
          if isinstance(style, list) or style is None:
              if isinstance(images, _tc.SFrame):
                  image_feature = _tkutl._find_only_image_column(images)
                  stylized_images = self.__proxy__.predict(images[image_feature], options)
                  stylized_images = stylized_images.rename(
                      {"stylized_image": "stylized_" + str(image_feature)}
                  )
                  return stylized_images
              return self.__proxy__.predict(images, options)
          else:
              if isinstance(images, _tc.SFrame):
                  if len(images) == 0:
                      raise _ToolkitError("SFrame cannot be empty")
                  image_feature = _tkutl._find_only_image_column(images)
                  stylized_images = self.__proxy__.predict(images[image_feature], options)
                  stylized_images = stylized_images.rename(
                      {"stylized_image": "stylized_" + str(image_feature)}
                  )
                  return stylized_images
              elif isinstance(images, (_tc.Image)):
                  stylized_images = self.__proxy__.predict(images, options)
                  return stylized_images["stylized_image"][0]
              elif isinstance(images, (_tc.SArray)):
                  stylized_images = self.__proxy__.predict(images, options)
                  return stylized_images["stylized_image"]
function_tokens: def stylize ( self , images , style = None , verbose = True , max_size = 800 , batch_size = 4 ) : if not isinstance ( images , ( _tc . SFrame , _tc . SArray , _tc . Image ) ) : raise TypeError ( '"image" parameter must be of type SFrame, SArray or turicreate.Image.' ) if isinstance ( images , ( _tc . SFrame , _tc . SArray ) ) and len ( images ) == 0 : raise _ToolkitError ( '"image" parameter cannot be empty' ) if style is not None and not isinstance ( style , ( int , list ) ) : raise TypeError ( '"style" must parameter must be a None, int or a list' ) if not isinstance ( max_size , int ) : raise TypeError ( '"max_size" must parameter must be an int' ) if max_size < 1 : raise _ToolkitError ( "'max_size' must be greater than or equal to 1" ) if not isinstance ( batch_size , int ) : raise TypeError ( '"batch_size" must parameter must be an int' ) if batch_size < 1 : raise _ToolkitError ( "'batch_size' must be greater than or equal to 1" ) options = { } options [ "style_idx" ] = style options [ "verbose" ] = verbose options [ "max_size" ] = max_size options [ "batch_size" ] = batch_size if isinstance ( style , list ) or style is None : if isinstance ( images , _tc . SFrame ) : image_feature = _tkutl . _find_only_image_column ( images ) stylized_images = self . __proxy__ . predict ( images [ image_feature ] , options ) stylized_images = stylized_images . rename ( { "stylized_image" : "stylized_" + str ( image_feature ) } ) return stylized_images return self . __proxy__ . predict ( images , options ) else : if isinstance ( images , _tc . SFrame ) : if len ( images ) == 0 : raise _ToolkitError ( "SFrame cannot be empty" ) image_feature = _tkutl . _find_only_image_column ( images ) stylized_images = self . __proxy__ . predict ( images [ image_feature ] , options ) stylized_images = stylized_images . rename ( { "stylized_image" : "stylized_" + str ( image_feature ) } ) return stylized_images elif isinstance ( images , ( _tc . Image ) ) : stylized_images = self . __proxy__ . predict ( images , options ) return stylized_images [ "stylized_image" ] [ 0 ] elif isinstance ( images , ( _tc . SArray ) ) : stylized_images = self . __proxy__ . predict ( images , options ) return stylized_images [ "stylized_image" ]