parameters: (tensors)
return_statement: return _apply_all_reduce('sum', tensors)
docstring: Returns a list of tensors with the all-reduce sum across `tensors`.
  
    The computation is done with an all-reduce operation, so if only some of the
    returned tensors are evaluated then the computation will hang.
  
    Args:
      tensors: The input tensors across which to sum; must be assigned
        to GPU devices.
  
    Returns:
      List of tensors, each with the sum of the input tensors, where tensor i has
      the same device as `tensors[i]`.
docstring_summary: Returns a list of tensors with the all-reduce sum across `tensors`.
docstring_tokens: Returns a list of tensors with the all - reduce sum across tensors .
function: def all_sum(tensors):
    """Returns a list of tensors with the all-reduce sum across `tensors`.
  
    The computation is done with an all-reduce operation, so if only some of the
    returned tensors are evaluated then the computation will hang.
  
    Args:
      tensors: The input tensors across which to sum; must be assigned
        to GPU devices.
  
    Returns:
      List of tensors, each with the sum of the input tensors, where tensor i has
      the same device as `tensors[i]`.
    """
    return _apply_all_reduce('sum', tensors)
function_tokens: def all_sum ( tensors ) : return _apply_all_reduce ( 'sum' , tensors )