parameters: (t1, out_format, dtype=None)
return_statement: return _compute_unary_elt_eise_op(f, t1, out_format, dtype)
docstring: Takes the cube root of each input in the tensor.
  
          Parameters
          ------------
          t1: tensor, array_like
              input tensor or array_like object
  
          out_format: format, mode_format, optional
              * If a :class:`format` is specified, the result tensor is stored in the format out_format.
              * If a :class:`mode_format` is specified, the result the result tensor has a with all of the dimensions
                stored in the :class:`mode_format` passed in.
  
          dtype: Datatype
              The datatype of the output tensor.
  
          Examples
          ----------
          >>> import pytaco as pt
          >>> pt.tensor_cube_root([4, -16, 9], out_format=pt.dense, dtype=pt.float32).toarray()
          array([ 1.587401 , -2.5198421,  2.0800838], dtype=float32)
  
          Returns
          --------
          cbrt: tensor
              The element wise cbrt of the input tensor.
docstring_summary: Takes the cube root of each input in the tensor.
docstring_tokens: Takes the cube root of each input in the tensor .
function: def tensor_cube_root(t1, out_format, dtype=None):
      """
          Takes the cube root of each input in the tensor.
  
          Parameters
          ------------
          t1: tensor, array_like
              input tensor or array_like object
  
          out_format: format, mode_format, optional
              * If a :class:`format` is specified, the result tensor is stored in the format out_format.
              * If a :class:`mode_format` is specified, the result the result tensor has a with all of the dimensions
                stored in the :class:`mode_format` passed in.
  
          dtype: Datatype
              The datatype of the output tensor.
  
          Examples
          ----------
          >>> import pytaco as pt
          >>> pt.tensor_cube_root([4, -16, 9], out_format=pt.dense, dtype=pt.float32).toarray()
          array([ 1.587401 , -2.5198421,  2.0800838], dtype=float32)
  
          Returns
          --------
          cbrt: tensor
              The element wise cbrt of the input tensor.
  
      """
  
      t1 = as_tensor(t1, copy=False)
      cast_val = _cm.max_type(_cm.float32, t1.dtype)
      f = lambda x: _cm.cube_root(_cm.cast(x, cast_val))
      return _compute_unary_elt_eise_op(f, t1, out_format, dtype)
function_tokens: def tensor_cube_root ( t1 , out_format , dtype = None ) : t1 = as_tensor ( t1 , copy = False ) cast_val = _cm . max_type ( _cm . float32 , t1 . dtype ) f = lambda x : _cm . cube_root ( _cm . cast ( x , cast_val ) ) return _compute_unary_elt_eise_op ( f , t1 , out_format , dtype )