code: def std(
      a,
      axis=None,
      dtype=None,
      out=None,
      ddof=0,
      keepdims=None,
      combine_size=None,
  ):
      """
      Compute the standard deviation along the specified axis.
  
      Returns the standard deviation, a measure of the spread of a distribution,
      of the tensor elements. The standard deviation is computed for the
      flattened tensor by default, otherwise over the specified axis.
  
      Parameters
      ----------
      a : array_like
          Calculate the standard deviation of these values.
      axis : None or int or tuple of ints, optional
          Axis or axes along which the standard deviation is computed. The
          default is to compute the standard deviation of the flattened tensor.
  
          If this is a tuple of ints, a standard deviation is performed over
          multiple axes, instead of a single axis or all the axes as before.
      dtype : dtype, optional
          Type to use in computing the standard deviation. For tensors of
          integer type the default is float64, for tensors of float types it is
          the same as the array type.
      out : Tensor, optional
          Alternative output tensor in which to place the result. It must have
          the same shape as the expected output but the type (of the calculated
          values) will be cast if necessary.
      ddof : int, optional
          Means Delta Degrees of Freedom.  The divisor used in calculations
          is ``N - ddof``, where ``N`` represents the number of elements.
          By default `ddof` is zero.
      keepdims : bool, optional
          If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one. With this option,
          the result will broadcast correctly against the input tensor.
  
          If the default value is passed, then `keepdims` will not be
          passed through to the `std` method of sub-classes of
          `Tensor`, however any non-default value will be.  If the
          sub-classes `sum` method does not implement `keepdims` any
          exceptions will be raised.
      combine_size: int, optional
          The number of chunks to combine.
  
      Returns
      -------
      standard_deviation : Tensor, see dtype parameter above.
          If `out` is None, return a new tensor containing the standard deviation,
          otherwise return a reference to the output array.
  
      See Also
      --------
      var, mean, nanmean, nanstd, nanvar
  
      Notes
      -----
      The standard deviation is the square root of the average of the squared
      deviations from the mean, i.e., ``std = sqrt(mean(abs(x - x.mean())**2))``.
  
      The average squared deviation is normally calculated as
      ``x.sum() / N``, where ``N = len(x)``.  If, however, `ddof` is specified,
      the divisor ``N - ddof`` is used instead. In standard statistical
      practice, ``ddof=1`` provides an unbiased estimator of the variance
      of the infinite population. ``ddof=0`` provides a maximum likelihood
      estimate of the variance for normally distributed variables. The
      standard deviation computed in this function is the square root of
      the estimated variance, so even with ``ddof=1``, it will not be an
      unbiased estimate of the standard deviation per se.
  
      Note that, for complex numbers, `std` takes the absolute
      value before squaring, so that the result is always real and nonnegative.
  
      For floating-point input, the *std* is computed using the same
      precision the input has. Depending on the input data, this can cause
      the results to be inaccurate, especially for float32 (see example below).
      Specifying a higher-accuracy accumulator using the `dtype` keyword can
      alleviate this issue.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> a = mt.array([[1, 2], [3, 4]])
      >>> mt.std(a).execute()
      1.1180339887498949
      >>> mt.std(a, axis=0).execute()
      array([ 1.,  1.])
      >>> mt.std(a, axis=1).execute()
      array([ 0.5,  0.5])
  
      In single precision, std() can be inaccurate:
  
      >>> a = mt.zeros((2, 512*512), dtype=mt.float32)
      >>> a[0, :] = 1.0
      >>> a[1, :] = 0.1
      >>> mt.std(a).execute()
      0.45000005
  
      Computing the standard deviation in float64 is more accurate:
  
      >>> mt.std(a, dtype=mt.float64).execute()
      0.44999999925494177
  
      """
      ret = sqrt(
          var(
              a,
              axis=axis,
              dtype=dtype,
              out=out,
              ddof=ddof,
              keepdims=keepdims,
              combine_size=combine_size,
          )
      )
      if dtype is not None and ret.dtype != dtype:
          ret = ret.astype(dtype)
      return ret
  
label_desc: Use image node_id 1 for calling a global function with example usage: std(a, axis, dtype, out, ddof, keepdims, combine_size) and returns: ret