class_docstr: Global max pooling operation for 3D data.
  
  Args:
      data_format: string, either `"channels_last"` or `"channels_first"`.
          The ordering of the dimensions in the inputs. `"channels_last"`
          corresponds to inputs with shape
          `(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
          while `"channels_first"` corresponds to inputs with shape
          `(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
          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"`.
      keepdims: A boolean, whether to keep the temporal dimension or not.
          If `keepdims` is `False` (default), the rank of the tensor is
          reduced for spatial dimensions. If `keepdims` is `True`, the
          spatial dimension are retained with length 1.
          The behavior is the same as for `tf.reduce_mean` or `np.mean`.
  
  Input shape:
  
  - If `data_format='channels_last'`:
      5D tensor with shape:
      `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
  - If `data_format='channels_first'`:
      5D tensor with shape:
      `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`
  
  Output shape:
  
  - If `keepdims=False`:
      2D tensor with shape `(batch_size, channels)`.
  - If `keepdims=True`:
      - If `data_format="channels_last"`:
          5D tensor with shape `(batch_size, 1, 1, 1, channels)`
      - If `data_format="channels_first"`:
          5D tensor with shape `(batch_size, channels, 1, 1, 1)`
  
  Example:
  
  >>> x = np.random.rand(2, 4, 5, 4, 3)
  >>> y = keras.layers.GlobalMaxPooling3D()(x)
  >>> y.shape
  (2, 3)
code: def call(self, inputs):
      if self.data_format == "channels_last":
          return ops.max(inputs, axis=[1, 2, 3], keepdims=self.keepdims)
      return ops.max(inputs, axis=[2, 3, 4], keepdims=self.keepdims)
  
imports: from keras import ops
  from keras.api_export import keras_export
  from keras.layers.pooling.base_global_pooling import BaseGlobalPooling
label_desc: Use image node_id 2 for calling the GlobalMaxPooling3D obj's underlying member method code with example usage: obj.call(inputs) and returns: ops, ops