class_docstr: Upsampling layer for 3D inputs.
  
  Repeats the 1st, 2nd and 3rd dimensions
  of the data by `size[0]`, `size[1]` and `size[2]` respectively.
  
  Example:
  
  >>> input_shape = (2, 1, 2, 1, 3)
  >>> x = np.ones(input_shape)
  >>> y = keras.layers.UpSampling3D(size=(2, 2, 2))(x)
  >>> y.shape
  (2, 2, 4, 2, 3)
  
  Args:
      size: Int, or tuple of 3 integers.
          The upsampling factors for dim1, dim2 and dim3.
      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, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
          while `"channels_first"` corresponds to inputs with shape
          `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
          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"`.
  
  Input shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, dim1, dim2, dim3, channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, dim1, dim2, dim3)`
  
  Output shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
          `(batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3,
          channels)`
      - If `data_format` is `"channels_first"`:
          `(batch_size, channels, upsampled_dim1, upsampled_dim2,
          upsampled_dim3)`
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code: def compute_output_shape(self, input_shape):
      if self.data_format == "channels_first":
          dim1 = (
              self.size[0] * input_shape[2]
              if input_shape[2] is not None
              else None
          )
          dim2 = (
              self.size[1] * input_shape[3]
              if input_shape[3] is not None
              else None
          )
          dim3 = (
              self.size[2] * input_shape[4]
              if input_shape[4] is not None
              else None
          )
          return (input_shape[0], input_shape[1], dim1, dim2, dim3)
      else:
          dim1 = (
              self.size[0] * input_shape[1]
              if input_shape[1] is not None
              else None
          )
          dim2 = (
              self.size[1] * input_shape[2]
              if input_shape[2] is not None
              else None
          )
          dim3 = (
              self.size[2] * input_shape[3]
              if input_shape[3] is not None
              else None
          )
          return (input_shape[0], dim1, dim2, dim3, input_shape[4])
  
file_path: keras/keras/layers/reshaping/up_sampling3d.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 2 for calling the UpSampling3D obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: input_shape,  input_shape,  dim1,  dim2,  dim3, input_shape,  dim1,  dim2,  dim3,  input_shape