class_docstr: Upsampling layer for 2D inputs.
  
  The implementation uses interpolative resizing, given the resize method
  (specified by the `interpolation` argument). Use `interpolation=nearest`
  to repeat the rows and columns of the data.
  
  Examples:
  
  >>> input_shape = (2, 2, 1, 3)
  >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
  >>> print(x)
  [[[[ 0  1  2]]
    [[ 3  4  5]]]
   [[[ 6  7  8]]
    [[ 9 10 11]]]]
  >>> y = keras.layers.UpSampling2D(size=(1, 2))(x)
  >>> print(y)
  [[[[ 0  1  2]
     [ 0  1  2]]
    [[ 3  4  5]
     [ 3  4  5]]]
   [[[ 6  7  8]
     [ 6  7  8]]
    [[ 9 10 11]
     [ 9 10 11]]]]
  
  Args:
      size: Int, or tuple of 2 integers.
          The upsampling factors for rows and columns.
      data_format: A string,
          one of `"channels_last"` (default) or `"channels_first"`.
          The ordering of the dimensions in the inputs.
          `"channels_last"` corresponds to inputs with shape
          `(batch_size, height, width, channels)` while `"channels_first"`
          corresponds to inputs with shape
          `(batch_size, channels, height, width)`.
          When unspecified, uses
          `image_data_format` value found in your Keras config file at
          `~/.keras/keras.json` (if exists) else `"channels_last"`.
          Defaults to `"channels_last"`.
      interpolation: A string, one of `"bicubic"`, `"bilinear"`, `"lanczos3"`,
          `"lanczos5"`, `"nearest"`.
  
  Input shape:
      4D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, rows, cols, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, rows, cols)`
  
  Output shape:
      4D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, upsampled_rows, upsampled_cols, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, upsampled_rows, upsampled_cols)`
class_docstr_tok: ["Upsampling","layer","for","2D","inputs",".","The","implementation","uses","interpolative","resizing",",","given","the","resize","method","(","specified","by","the","`","interpolation","`","argument",")",".","Use","`","interpolation=nearest","`","to","repeat","the","rows","and","columns","of","the","data",".","Examples",":",">",">",">","input_shape","=","(","2",",","2",",","1",",","3",")",">",">",">","x","=","np.arange","(","np.prod","(","input_shape",")",")",".reshape","(","input_shape",")",">",">",">","print","(","x",")","[","[","[","[","0","1","2","]","]","[","[","3","4","5","]","]","]","[","[","[","6","7","8","]","]","[","[","9","10","11","]","]","]","]",">",">",">","y","=","keras.layers.UpSampling2D","(","size=","(","1",",","2",")",")","(","x",")",">",">",">","print","(","y",")","[","[","[","[","0","1","2","]","[","0","1","2","]","]","[","[","3","4","5","]","[","3","4","5","]","]","]","[","[","[","6","7","8","]","[","6","7","8","]","]","[","[","9","10","11","]","[","9","10","11","]","]","]","]","Args",":","size",":","Int",",","or","tuple","of","2","integers",".","The","upsampling","factors","for","rows","and","columns",".","data_format",":","A","string",",","one","of","`","``","channels_last","''","`","(","default",")","or","`","``","channels_first","''","`",".","The","ordering","of","the","dimensions","in","the","inputs",".","`","``","channels_last","''","`","corresponds","to","inputs","with","shape","`","(","batch_size",",","height",",","width",",","channels",")","`","while","`","``","channels_first","''","`","corresponds","to","inputs","with","shape","`","(","batch_size",",","channels",",","height",",","width",")","`",".","When","unspecified",",","uses","`","image_data_format","`","value","found","in","your","Keras","config","file","at","`","~\/.keras\/keras.json","`","(","if","exists",")","else","`","``","channels_last","''","`",".","Defaults","to","`","``","channels_last","''","`",".","interpolation",":","A","string",",","one","of","`","``","bicubic","''","`",",","`","``","bilinear","''","`",",","`","``","lanczos3","''","`",",","`","``","lanczos5","''","`",",","`","``","nearest","''","`",".","Input","shape",":","4D","tensor","with","shape",":","-","If","`","data_format","`","is","`","``","channels_last","''","`",":","`","(","batch_size",",","rows",",","cols",",","channels",")","`","-","If","`","data_format","`","is","`","``","channels_first","''","`",":","`","(","batch_size",",","channels",",","rows",",","cols",")","`","Output","shape",":","4D","tensor","with","shape",":","-","If","`","data_format","`","is","`","``","channels_last","''","`",":","`","(","batch_size",",","upsampled_rows",",","upsampled_cols",",","channels",")","`","-","If","`","data_format","`","is","`","``","channels_first","''","`",":","`","(","batch_size",",","channels",",","upsampled_rows",",","upsampled_cols",")","`"]
code: def get_config(self):
      config = {
          "size": self.size,
          "data_format": self.data_format,
          "interpolation": self.interpolation,
      }
      base_config = super().get_config()
      return {**base_config, **config}
  
file_path: keras/keras/layers/reshaping/up_sampling2d.py
imports: from keras import backend
  from keras import ops
  from keras.api_export import keras_export
  from keras.layers.input_spec import InputSpec
  from keras.layers.layer import Layer
  from keras.utils import argument_validation
label_desc: Use image node_id 4 for calling the UpSampling2D obj's underlying member method code with example usage: obj.get_config() and returns: dict