docstr: ivy.Container static method variant of ivy.avg_pool3d. This method
  simply wraps the function, and so the docstring for ivy.avg_pool3d also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      Input volume *[batch_size,d,h,w,d_in]*.
  kernel
      Convolution filters *[d,h,w]*.
  strides
      The stride of the sliding window for each dimension of input.
  padding
      SAME" or "VALID" indicating the algorithm, or list indicating
      the per-dimension paddings.
  data_format
      NDHWC" or "NCDHW". Defaults to "NDHWC".
  count_include_pad
      Whether to include padding in the averaging calculation.
  ceil_mode
      Whether to use ceil or floor for creating the output shape.
  divisor_override
      If specified, it will be used as the divisor, otherwise
  out
      optional output array, for writing the result to. It must
      have a shape that the inputs broadcast to.
  
  Returns
  -------
  ret
      The result of the pooling operation.
  
  Examples
  --------
  >>> a = ivy.arange(24.).reshape((1, 2, 3, 4, 1))
  >>> b = ivy.arange(48.).reshape((2, 4, 3, 2, 1))
  >>> x = ivy.Container(a=a, b=b)
  >>> print(x.avg_pool3d(2, 1, "VALID"))
  {
      a: ivy.array([[[[[8.5],
                       [9.5],
                       [10.5]],
                      [[12.5],
                       [13.5],
                       [14.5]]]]]),
      b: (<class ivy.data_classes.array.array.Array> shape=[2, 3, 2, 1, 1])
  }
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code: def avg_pool3d(
      self: ivy.Container,
      kernel: Union[
          int, Tuple[int], Tuple[int, int, int], ivy.Container
      ],
      strides: Union[
          int, Tuple[int], Tuple[int, int, int], ivy.Container
      ],
      padding: Union[str, ivy.Container],
      /,
      *,
      data_format: Union[str, ivy.Container] = "NDHWC",
      count_include_pad: Union[bool, ivy.Container] = False,
      ceil_mode: Union[bool, ivy.Container] = False,
      divisor_override: Optional[Union[int, ivy.Container]] = None,
      key_chains: Optional[
          Union[List[str], Dict[str, str], ivy.Container]
      ] = None,
      to_apply: Union[bool, ivy.Container] = True,
      prune_unapplied: Union[bool, ivy.Container] = False,
      map_sequences: Union[bool, ivy.Container] = False,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container static method variant of ivy.avg_pool3d. This method
      simply wraps the function, and so the docstring for ivy.avg_pool3d also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          Input volume *[batch_size,d,h,w,d_in]*.
      kernel
          Convolution filters *[d,h,w]*.
      strides
          The stride of the sliding window for each dimension of input.
      padding
          SAME" or "VALID" indicating the algorithm, or list indicating
          the per-dimension paddings.
      data_format
          NDHWC" or "NCDHW". Defaults to "NDHWC".
      count_include_pad
          Whether to include padding in the averaging calculation.
      ceil_mode
          Whether to use ceil or floor for creating the output shape.
      divisor_override
          If specified, it will be used as the divisor, otherwise
      out
          optional output array, for writing the result to. It must
          have a shape that the inputs broadcast to.
  
      Returns
      -------
      ret
          The result of the pooling operation.
  
      Examples
      --------
      >>> a = ivy.arange(24.).reshape((1, 2, 3, 4, 1))
      >>> b = ivy.arange(48.).reshape((2, 4, 3, 2, 1))
      >>> x = ivy.Container(a=a, b=b)
      >>> print(x.avg_pool3d(2, 1, "VALID"))
      {
          a: ivy.array([[[[[8.5],
                           [9.5],
                           [10.5]],
                          [[12.5],
                           [13.5],
                           [14.5]]]]]),
          b: (<class ivy.data_classes.array.array.Array> shape=[2, 3, 2, 1, 1])
      }
      """
      return self.static_avg_pool3d(
          self,
          kernel,
          strides,
          padding,
          data_format=data_format,
          count_include_pad=count_include_pad,
          ceil_mode=ceil_mode,
          divisor_override=divisor_override,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          out=out,
      )
  
imports: from typing import Optional, Union, List, Dict, Tuple, Literal, Sequence, Callable
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 12 for calling the _ContainerWithLayersExperimental obj's underlying member method code with example usage: obj.avg_pool3d() and returns: self