class_docstr: Spatial 3D version of Dropout.
  
  This version performs the same function as Dropout, however, it drops
  entire 3D feature maps instead of individual elements. If adjacent voxels
  within feature maps are strongly correlated (as is normally the case in
  early convolution layers) then regular dropout will not regularize the
  activations and will otherwise just result in an effective learning rate
  decrease. In this case, SpatialDropout3D will help promote independence
  between feature maps and should be used instead.
  
  Args:
      rate: Float between 0 and 1. Fraction of the input units to drop.
      data_format: `"channels_first"` or `"channels_last"`.
          In `"channels_first"` mode, the channels dimension (the depth)
          is at index 1, in `"channels_last"` mode is it at index 4.
          It defaults to the `image_data_format` value found in your
          Keras config file at `~/.keras/keras.json`.
          If you never set it, then it will be `"channels_last"`.
  
  Call arguments:
      inputs: A 5D tensor.
      training: Python boolean indicating whether the layer
              should behave in training mode (applying dropout)
              or in inference mode (pass-through).
  
  Input shape:
      5D tensor with shape: `(samples, channels, dim1, dim2, dim3)` if
          data_format='channels_first'
      or 5D tensor with shape: `(samples, dim1, dim2, dim3, channels)` if
          data_format='channels_last'.
  
  Output shape: Same as input.
  
  Reference:
  
  - [Tompson et al., 2014](https://arxiv.org/abs/1411.4280)
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code: def _get_noise_shape(self, inputs):
      input_shape = ops.shape(inputs)
      if self.data_format == "channels_first":
          return (input_shape[0], input_shape[1], 1, 1, 1)
      elif self.data_format == "channels_last":
          return (input_shape[0], 1, 1, 1, input_shape[4])
  
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.regularization.dropout import Dropout
label_desc: Use image node_id 2 for calling the SpatialDropout3D obj's underlying member method code with example usage: obj._get_noise_shape(inputs) and returns: input_shape,  input_shape,  int,  int,  int, input_shape,  int,  int,  int,  input_shape