parameters: (
      inp: Tensor,
      axis: Optional[Union[int, Sequence[int]]] = None,
      keepdims: bool = False,
  )
return_statement: return inp.mean(axis=axis, keepdims=keepdims)
docstring: r"""Returns the mean value of input tensor along
      given axis. If axis is a list of dimensions,
      reduce over all of them.
  
      Args:
          inp: input tensor.
          axis: dimension to reduce. If None, all dimensions will be reduced. Default: None
          keepdims: whether the output tensor has axis retained or not. Default: False
  
      Returns:
          output tensor.
  
      Examples:
  
          .. testcode::
  
              import numpy as np
              from megengine import tensor
              import megengine.functional as F
  
              x = tensor(np.arange(1, 7, dtype=np.int32).reshape(2, 3))
              out = F.mean(x)
              print(out.numpy())
  
          Outputs:
  
          .. testoutput::
  
              3.5
docstring_summary: r"""Returns the mean value of input tensor along
      given axis. If axis is a list of dimensions,
      reduce over all of them.
docstring_tokens: r Returns the mean value of input tensor along given axis . If axis is a list of dimensions reduce over all of them .
function: def mean(
      inp: Tensor,
      axis: Optional[Union[int, Sequence[int]]] = None,
      keepdims: bool = False,
  ) -> Tensor:
      r"""Returns the mean value of input tensor along
      given axis. If axis is a list of dimensions,
      reduce over all of them.
  
      Args:
          inp: input tensor.
          axis: dimension to reduce. If None, all dimensions will be reduced. Default: None
          keepdims: whether the output tensor has axis retained or not. Default: False
  
      Returns:
          output tensor.
  
      Examples:
  
          .. testcode::
  
              import numpy as np
              from megengine import tensor
              import megengine.functional as F
  
              x = tensor(np.arange(1, 7, dtype=np.int32).reshape(2, 3))
              out = F.mean(x)
              print(out.numpy())
  
          Outputs:
  
          .. testoutput::
  
              3.5
      """
      return inp.mean(axis=axis, keepdims=keepdims)
function_tokens: def mean ( inp : Tensor , axis : Optional [ Union [ int , Sequence [ int ] ] ] = None , keepdims : bool = False , ) -> Tensor : return inp . mean ( axis = axis , keepdims = keepdims )