code: def _std(x, axis, correction, keepdim):
      u = paddle_backend.mean(x, axis=axis, keepdims=True)
      out = paddle_backend.sum(
          paddle_backend.pow(paddle_backend.subtract(x, u), 2),
          axis=axis,
          keepdims=keepdim,
      )
      num_elm_in = paddle.prod(paddle.to_tensor(x.shape)).item()
      num_elm_out = paddle.prod(paddle.to_tensor(out.shape)).item()
      n = num_elm_out / num_elm_in
      out = paddle_backend.sqrt(paddle_backend.multiply(out, n))
      if correction:
          n = paddle_backend.sqrt(
              paddle_backend.divide(
                  num_elm_in, (num_elm_in - correction * num_elm_out)
              )
          )
          out = paddle_backend.multiply(out, n)
      return out
  
file_path: ivy/ivy/functional/backends/paddle/statistical.py
imports: from typing import Union, Optional, Sequence
  import paddle
  import ivy
  from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes, with_supported_device_and_dtypes
  import ivy.functional.backends.paddle
  from ivy.utils.einsum_parser import legalise_einsum_expr
  from .None import backend_version
label_desc: Use image node_id 5 for calling a global function with example usage: _std(x, axis, correction, keepdim) and returns: out