docstr: ivy.Container static method variant of ivy.avg_pool1d. This method
  simply wraps the function, and so the docstring for ivy.avg_pool1d also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      Container of input images *[batch_size, w, d_in]*.
  kernel
      Size of the kernel i.e., the sliding window for each
      dimension of input. *[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
      NWC" or "NCW". Defaults to "NWC".
  count_include_pad
      Whether to include padding in the averaging calculation.
  ceil_mode
      Whether to use ceil or floor for creating the output shape.
  out
      optional output array, for writing the result to.
  
  Returns
  -------
  ret
      The result of the pooling operation.
  
  Examples
  --------
  >>> a = ivy.arange(12.).reshape((2,2,3))
  >>> b = ivy.arange(24.).reshape((2,3,4))
  >>> x = ivy.Container({'a': a, 'b': b})
  >>> print(ivy.Container.static_avg_pool1d(x,2, 2, "VALID"))
  {
      a: ivy.array([[[1.5, 2.5, 3.5]],
                    [[7.5, 8.5, 9.5]]]),
      b: ivy.array([[[2., 3., 4., 5.]],
                    [[14., 15., 16., 17.]]])
  }
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code: def static_avg_pool1d(
      x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      kernel: Union[int, Tuple[int], ivy.Container],
      strides: Union[int, Tuple[int], ivy.Container],
      padding: Union[str, ivy.Container],
      /,
      *,
      data_format: Union[str, ivy.Container] = "NWC",
      count_include_pad: Union[bool, ivy.Container] = False,
      ceil_mode: Union[bool, ivy.Container] = False,
      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_pool1d. This method
      simply wraps the function, and so the docstring for ivy.avg_pool1d also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          Container of input images *[batch_size, w, d_in]*.
      kernel
          Size of the kernel i.e., the sliding window for each
          dimension of input. *[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
          NWC" or "NCW". Defaults to "NWC".
      count_include_pad
          Whether to include padding in the averaging calculation.
      ceil_mode
          Whether to use ceil or floor for creating the output shape.
      out
          optional output array, for writing the result to.
  
      Returns
      -------
      ret
          The result of the pooling operation.
  
      Examples
      --------
      >>> a = ivy.arange(12.).reshape((2,2,3))
      >>> b = ivy.arange(24.).reshape((2,3,4))
      >>> x = ivy.Container({'a': a, 'b': b})
      >>> print(ivy.Container.static_avg_pool1d(x,2, 2, "VALID"))
      {
          a: ivy.array([[[1.5, 2.5, 3.5]],
                        [[7.5, 8.5, 9.5]]]),
          b: ivy.array([[[2., 3., 4., 5.]],
                        [[14., 15., 16., 17.]]])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "avg_pool1d",
          x,
          kernel,
          strides,
          padding,
          data_format=data_format,
          count_include_pad=count_include_pad,
          ceil_mode=ceil_mode,
          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 7 for calling the _ContainerWithLayersExperimental obj's underlying member method code with example usage: obj.static_avg_pool1d() and returns: ContainerBase