docstr: ivy.Container static method variant of ivy.max_unpool1d.
  
  Parameters
  ----------
  input
      Pooled input image *[batch_size, w, d_in]*.
  indices
      Indices obtained from the corresponding max pooling operation.
  kernel_size
      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 "NCW".
  key_chains
      The key-chains to apply or not apply the method to. Default is ``None``.
  to_apply
      If True, the method will be applied to key_chains, otherwise key_chains
      will be skipped. Default is ``True``.
  prune_unapplied
      Whether to prune key_chains for which the function was not applied.
      Default is ``False``.
  map_sequences
      Whether to also map method to sequences (lists, tuples).
      Default is ``False``.
  
  Returns
  -------
  ret
      The result of the unpooling operation.
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code: def static_max_unpool1d(
      input: ivy.Container,
      indices: ivy.Container,
      kernel_size: Union[Tuple[int], int],
      /,
      *,
      strides: Optional[Union[int, Tuple[int]]] = None,
      padding: Union[int, Tuple[int]] = 0,
      data_format: Union[str, ivy.Container] = "NCW",
      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,
  ) -> ivy.Container:
      """ivy.Container static method variant of ivy.max_unpool1d.
  
      Parameters
      ----------
      input
          Pooled input image *[batch_size, w, d_in]*.
      indices
          Indices obtained from the corresponding max pooling operation.
      kernel_size
          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 "NCW".
      key_chains
          The key-chains to apply or not apply the method to. Default is ``None``.
      to_apply
          If True, the method will be applied to key_chains, otherwise key_chains
          will be skipped. Default is ``True``.
      prune_unapplied
          Whether to prune key_chains for which the function was not applied.
          Default is ``False``.
      map_sequences
          Whether to also map method to sequences (lists, tuples).
          Default is ``False``.
  
      Returns
      -------
      ret
          The result of the unpooling operation.
      """
      return ContainerBase.cont_multi_map_in_function(
          "max_unpool1d",
          input,
          indices,
          kernel_size,
          strides=strides,
          padding=padding,
          data_format=data_format,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
      )
  
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 43 for calling the _ContainerWithLayersExperimental obj's underlying member method code with example usage: obj.static_max_unpool1d() and returns: ContainerBase