parameters: (input: Tensor, output_size: BroadcastingList2[int])
return_statement: return torch._C._nn.adaptive_avg_pool2d(input, _output_size)
docstring: r"""
      Applies a 2D adaptive average pooling over an input signal composed of
      several input planes.
  
      See :class:`~torch.nn.AdaptiveAvgPool2d` for details and output shape.
  
      Args:
          output_size: the target output size (single integer or
              double-integer tuple)
docstring_summary: r"""
      Applies a 2D adaptive average pooling over an input signal composed of
      several input planes.
docstring_tokens: r Applies a 2D adaptive average pooling over an input signal composed of several input planes .
function: def adaptive_avg_pool2d(input: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
      r"""
      Applies a 2D adaptive average pooling over an input signal composed of
      several input planes.
  
      See :class:`~torch.nn.AdaptiveAvgPool2d` for details and output shape.
  
      Args:
          output_size: the target output size (single integer or
              double-integer tuple)
      """
      if has_torch_function_unary(input):
          return handle_torch_function(adaptive_avg_pool2d, (input,), input, output_size)
      _output_size = _list_with_default(output_size, input.size())
      return torch._C._nn.adaptive_avg_pool2d(input, _output_size)
function_tokens: def adaptive_avg_pool2d ( input : Tensor , output_size : BroadcastingList2 [ int ] ) -> Tensor : if has_torch_function_unary ( input ) : return handle_torch_function ( adaptive_avg_pool2d , ( input , ) , input , output_size ) _output_size = _list_with_default ( output_size , input . size ( ) ) return torch . _C . _nn . adaptive_avg_pool2d ( input , _output_size )