code: def avg_pool_3d(
      incoming, kernel_size, strides=1, padding="same", name="AvgPool3D"
  ):
      """Average Pooling 3D.
  
      Input:
          5-D Tensor [batch, depth, rows, cols, channels].
  
      Output:
          5-D Tensor [batch, pooled depth, pooled rows, pooled cols, in_channels].
  
      Arguments:
          incoming: `Tensor`. Incoming 5-D Layer.
          kernel_size: `int` or `list of int`. Pooling kernel size.
              Must have kernel_size[0] = kernel_size[1] = 1
          strides: `int` or `list of int`. Strides of conv operation.
              Must have strides[0] = strides[4] = 1.
              Default: [1 1 1 1 1]
          padding: `str` from `"same", "valid"`. Padding algo to use.
              Default: 'same'.
          name: A name for this layer (optional). Default: 'AvgPool3D'.
  
      Attributes:
          scope: `Scope`. This layer scope.
  
      """
      input_shape = utils.get_incoming_shape(incoming)
      assert (
          len(input_shape) == 5
      ), "Incoming Tensor shape must be 5-D, not %d-D" % len(
          input_shape
      )
  
      kernel = utils.autoformat_kernel_3d(kernel_size)
      strides = utils.autoformat_stride_3d(strides)
      padding = utils.autoformat_padding(padding)
  
      with tf.name_scope(name) as scope:
          inference = tf.nn.avg_pool3d(
              incoming, kernel, strides, padding
          )
  
          # Track activations.
          tf.add_to_collection(tf.GraphKeys.ACTIVATIONS, inference)
  
      # Add attributes to Tensor to easy access weights
      inference.scope = scope
  
      # Track output tensor.
      tf.add_to_collection(
          tf.GraphKeys.LAYER_TENSOR + "/" + name, inference
      )
  
      return inference
  
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  import numpy
  from math import ceil
  import tflearn
  from ..None import variables
  from ..None import activations
  from ..None import initializations
  from ..None import regularizers
  from ..None import utils
  from ..layers.normalization import batch_normalization
label_desc: Use image node_id 16 for calling a global function with example usage: avg_pool_3d(incoming, kernel_size, strides, padding, name) and returns: inference