docstr: Apply a 2D adaptive maximum pooling over an input signal composed of
  several input planes.
  
  Parameters
  ----------
  self
      Input container.
  output_size
      Spatial output size.
  
  Returns
  -------
      The result of the pooling operation.
docstr_tok: ["Apply","a","2D","adaptive","maximum","pooling","over","an","input","signal","composed","of","several","input","planes",".","Parameters","--","--","--","--","--","self","Input","container",".","output_size","Spatial","output","size",".","Returns","--","--","--","-","The","result","of","the","pooling","operation","."]
code: def adaptive_max_pool2d(
      self: ivy.Container,
      output_size: Union[int, ivy.Container],
      *,
      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:
      """Apply a 2D adaptive maximum pooling over an input signal composed of
      several input planes.
  
      Parameters
      ----------
      self
          Input container.
      output_size
          Spatial output size.
  
      Returns
      -------
          The result of the pooling operation.
      """
      return self.static_adaptive_max_pool2d(
          self,
          output_size,
          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 32 for calling the _ContainerWithLayersExperimental obj's underlying member method code with example usage: obj.adaptive_max_pool2d(output_size) and returns: self