class_docstr: ConvNextForImageClassification is an image classifier based on ConvNet models.
  
  The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan
  Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. ConvNeXT is a pure convolutional
  model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform
  them.
  
  For available pretrained models please see the
  `Models Hub <https://sparknlp.org/models?task=Image+Classification>`__.
  
  Models from the HuggingFace 🤗 Transformers library are also compatible with Spark
  NLP 🚀. To see which models are compatible and how to import them see
  https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended
  examples, see
  `ConvNextForImageClassificationTestSpec <https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/ConvNextForImageClassificationTestSpec.scala>`__.
  
  ====================== ======================
  Input Annotation types Output Annotation type
  ====================== ======================
  ``IMAGE``              ``CATEGORY``
  ====================== ======================
  
  **Paper Abstract:**
  
  *The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly
  superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces
  difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is
  the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making
  Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide
  variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the
  intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we
  reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a
  standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to
  the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed
  ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms
  of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO
  detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets. *
  
  References
  ----------
  
  `A ConvNet for the 2020s <https://arxiv.org/abs/2201.03545>`__
  
  Parameters
  ----------
  doResize
      Whether to resize the input to a certain size
  doNormalize
      Whether to normalize the input with mean and standard deviation
  featureExtractorType
      Name of model's architecture for feature extraction
  imageMean
      The sequence of means for each channel, to be used when normalizing images
  imageStd
      The sequence of standard deviations for each channel, to be used when normalizing images
  resample
      An optional resampling filter. This can be one of `PIL.Image.NEAREST`, `PIL.Image.BILINEAR` or
      `PIL.Image.BICUBIC`. Only has an effect if do_resize is set to True.
  size
      Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an integer is
      provided, then the input will be resized to (size, size). Only has an effect if do_resize is set to True.
  doRescale
      Whether to rescale the image values by rescaleFactor
  rescaleFactor
      Factor to scale the image values
  cropPct
      Percentage of the resized image to crop
  configProtoBytes
      ConfigProto from tensorflow, serialized into byte array.
  
  Examples
  --------
  >>> import sparknlp
  >>> from sparknlp.base import *
  >>> from sparknlp.annotator import *
  >>> from pyspark.ml import Pipeline
  >>> imageDF = spark.read \
  ...     .format("image") \
  ...     .option("dropInvalid", value = True) \
  ...     .load("src/test/resources/image/")
  >>> imageAssembler = ImageAssembler() \
  ...     .setInputCol("image") \
  ...     .setOutputCol("image_assembler")
  >>> imageClassifier = ConvNextForImageClassification \
  ...     .pretrained() \
  ...     .setInputCols(["image_assembler"]) \
  ...     .setOutputCol("class")
  >>> pipeline = Pipeline().setStages([imageAssembler, imageClassifier])
  >>> pipelineDF = pipeline.fit(imageDF).transform(imageDF)
  >>> pipelineDF \
  ...   .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result") \
  ...   .show(truncate=False)
  +-----------------+----------------------------------------------------------+
  |image_name       |result                                                    |
  +-----------------+----------------------------------------------------------+
  |bluetick.jpg     |[bluetick]                                                |
  |chihuahua.jpg    |[Chihuahua]                                               |
  |egyptian_cat.jpeg|[tabby, tabby cat]                                        |
  |hen.JPEG         |[hen]                                                     |
  |hippopotamus.JPEG|[hippopotamus, hippo, river horse, Hippopotamus amphibius]|
  |junco.JPEG       |[junco, snowbird]                                         |
  |ostrich.JPEG     |[ostrich, Struthio camelus]                               |
  |ox.JPEG          |[ox]                                                      |
  |palace.JPEG      |[palace]                                                  |
  |tractor.JPEG     |[thresher, thrasher, threshing machine                    |
  +-----------------+----------------------------------------------------------+
docstr: Sets Whether to rescale the image values by rescaleFactor, by default `True`.
  
  Parameters
  ----------
  value : Boolean
      Whether to rescale the image values by rescaleFactor.
docstr_tok: ["Sets","Whether","to","rescale","the","image","values","by","rescaleFactor",",","by","default","`","True","`",".","Parameters","--","--","--","--","--","value",":","Boolean","Whether","to","rescale","the","image","values","by","rescaleFactor","."]
code: def setDoRescale(self, value):
      """Sets Whether to rescale the image values by rescaleFactor, by default `True`.
  
      Parameters
      ----------
      value : Boolean
          Whether to rescale the image values by rescaleFactor.
      """
      return self._set(doRescale=value)
  
file_path: spark-nlp/python/sparknlp/annotator/cv/convnext_for_image_classification.py
imports: from sparknlp.common import *
label_desc: Use image node_id 1 for calling the ConvNextForImageClassification obj's underlying member method code with example usage: obj.setDoRescale(value) and returns: self