code: def subtract(x1, x2, out=None, where=None, **kwargs):
      """
      Subtract arguments, element-wise.
  
      Parameters
      ----------
      x1, x2 : array_like
          The tensors to be subtracted from each other.
      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
          The difference of `x1` and `x2`, element-wise.  Returns a scalar if
          both  `x1` and `x2` are scalars.
  
      Notes
      -----
      Equivalent to ``x1 - x2`` in terms of tensor broadcasting.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.subtract(1.0, 4.0).execute()
      -3.0
  
      >>> x1 = mt.arange(9.0).reshape((3, 3))
      >>> x2 = mt.arange(3.0)
      >>> mt.subtract(x1, x2).execute()
      array([[ 0.,  0.,  0.],
             [ 3.,  3.,  3.],
             [ 6.,  6.,  6.]])
      """
      op = TensorSubtract(**kwargs)
      return op(x1, x2, out=out, where=where)
  
file_path: mars/mars/tensor/arithmetic/subtract.py
imports: import numpy
  from ...None import opcodes
  from ..utils import infer_dtype
  from .core import TensorBinOp
  from .utils import arithmetic_operand
label_desc: Use image node_id 1 for calling a global function with example usage: subtract(x1, x2, out, where) and returns: op