code: def sqrt(x, out=None, where=None, **kwargs):
      """
      Return the positive square-root of an tensor, element-wise.
  
      Parameters
      ----------
      x : array_like
          The values whose square-roots are required.
      out : Tensor, None, or tuple of Tensor and None, optional
          A location into which the result is stored. If provided, it must have
          a shape that the inputs broadcast to. If not provided or `None`,
          a freshly-allocated tensor is returned. A tuple (possible only as a
          keyword argument) must have length equal to the number of outputs.
      where : array_like, optional
          Values of True indicate to calculate the ufunc at that position, values
          of False indicate to leave the value in the output alone.
      **kwargs
  
      Returns
      -------
      y : Tensor
          An tensor of the same shape as `x`, containing the positive
          square-root of each element in `x`.  If any element in `x` is
          complex, a complex tensor is returned (and the square-roots of
          negative reals are calculated).  If all of the elements in `x`
          are real, so is `y`, with negative elements returning ``nan``.
          If `out` was provided, `y` is a reference to it.
  
      Notes
      -----
      *sqrt* has--consistent with common convention--as its branch cut the
      real "interval" [`-inf`, 0), and is continuous from above on it.
      A branch cut is a curve in the complex plane across which a given
      complex function fails to be continuous.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.sqrt([1,4,9]).execute()
      array([ 1.,  2.,  3.])
  
      >>> mt.sqrt([4, -1, -3+4J]).execute()
      array([ 2.+0.j,  0.+1.j,  1.+2.j])
  
      >>> mt.sqrt([4, -1, mt.inf]).execute()
      array([  2.,  NaN,  Inf])
      """
      op = TensorSqrt(**kwargs)
      return op(x, out=out, where=where)
  
file_path: mars/mars/tensor/arithmetic/sqrt.py
imports: import numpy
  from ...None import opcodes
  from ..utils import infer_dtype
  from .core import TensorUnaryOp
  from .utils import arithmetic_operand
label_desc: Use image node_id 1 for calling a global function with example usage: sqrt(x, out, where) and returns: op