code: def multiply(x1, x2, out=None, where=None, **kwargs):
      """
      Multiply arguments element-wise.
  
      Parameters
      ----------
      x1, x2 : array_like
          Input arrays to be multiplied.
      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 product of `x1` and `x2`, element-wise. Returns a scalar if
          both  `x1` and `x2` are scalars.
  
      Notes
      -----
      Equivalent to `x1` * `x2` in terms of array broadcasting.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.multiply(2.0, 4.0).execute()
      8.0
  
      >>> x1 = mt.arange(9.0).reshape((3, 3))
      >>> x2 = mt.arange(3.0)
      >>> mt.multiply(x1, x2).execute()
      array([[  0.,   1.,   4.],
             [  0.,   4.,  10.],
             [  0.,   7.,  16.]])
      """
      op = TensorMultiply(**kwargs)
      return op(x1, x2, out=out, where=where)
  
file_path: mars/mars/tensor/arithmetic/multiply.py
imports: import numpy
  from functools import reduce
  from ...None import opcodes
  from ...serialization.serializables import BoolField
  from ..array_utils import device, as_same_device
  from ..datasource import scalar
  from ..utils import infer_dtype
  from .core import TensorBinOp, TensorMultiOp
  from .utils import arithmetic_operand, tree_op_estimate_size, TreeReductionBuilder
label_desc: Use image node_id 1 for calling a global function with example usage: multiply(x1, x2, out, where) and returns: op