parameters: (lhs, rhs)
return_statement: return _ufunc_helper(
          lhs,
          rhs,
          op.broadcast_mul,
          operator.mul,
          _internal._mul_scalar,
          None)
docstring: Returns element-wise product of the input arrays with broadcasting.
  
      Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.
  
      .. note::
  
         If the corresponding dimensions of two arrays have the same size or one of them has size 1,
         then the arrays are broadcastable to a common shape.
  
      Parameters
      ----------
      lhs : scalar or array
          First array to be multiplied.
      rhs : scalar or array
           Second array to be multiplied.
          If ``lhs.shape != rhs.shape``, they must be
          broadcastable to a common shape.
  
      Returns
      -------
      NDArray
          The element-wise multiplication of the input arrays.
  
      Examples
      --------
      >>> x = mx.nd.ones((2,3))
      >>> y = mx.nd.arange(2).reshape((2,1))
      >>> z = mx.nd.arange(2).reshape((1,2))
      >>> x.asnumpy()
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> y.asnumpy()
      array([[ 0.],
             [ 1.]], dtype=float32)
      >>> z.asnumpy()
      array([[ 0.,  1.]], dtype=float32)
      >>> (x*2).asnumpy()
      array([[ 2.,  2.,  2.],
             [ 2.,  2.,  2.]], dtype=float32)
      >>> (x*y).asnumpy()
      array([[ 0.,  0.,  0.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> mx.nd.multiply(x, y).asnumpy()
      array([[ 0.,  0.,  0.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> (z*y).asnumpy()
      array([[ 0.,  0.],
             [ 0.,  1.]], dtype=float32)
docstring_summary: Returns element-wise product of the input arrays with broadcasting.
docstring_tokens: Returns element - wise product of the input arrays with broadcasting .
function: def multiply(lhs, rhs):
      """Returns element-wise product of the input arrays with broadcasting.
  
      Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.
  
      .. note::
  
         If the corresponding dimensions of two arrays have the same size or one of them has size 1,
         then the arrays are broadcastable to a common shape.
  
      Parameters
      ----------
      lhs : scalar or array
          First array to be multiplied.
      rhs : scalar or array
           Second array to be multiplied.
          If ``lhs.shape != rhs.shape``, they must be
          broadcastable to a common shape.
  
      Returns
      -------
      NDArray
          The element-wise multiplication of the input arrays.
  
      Examples
      --------
      >>> x = mx.nd.ones((2,3))
      >>> y = mx.nd.arange(2).reshape((2,1))
      >>> z = mx.nd.arange(2).reshape((1,2))
      >>> x.asnumpy()
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> y.asnumpy()
      array([[ 0.],
             [ 1.]], dtype=float32)
      >>> z.asnumpy()
      array([[ 0.,  1.]], dtype=float32)
      >>> (x*2).asnumpy()
      array([[ 2.,  2.,  2.],
             [ 2.,  2.,  2.]], dtype=float32)
      >>> (x*y).asnumpy()
      array([[ 0.,  0.,  0.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> mx.nd.multiply(x, y).asnumpy()
      array([[ 0.,  0.,  0.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> (z*y).asnumpy()
      array([[ 0.,  0.],
             [ 0.,  1.]], dtype=float32)
      """
      # pylint: disable= no-member, protected-access
      return _ufunc_helper(
          lhs,
          rhs,
          op.broadcast_mul,
          operator.mul,
          _internal._mul_scalar,
          None)
function_tokens: def multiply ( lhs , rhs ) : # pylint: disable= no-member, protected-access return _ufunc_helper ( lhs , rhs , op . broadcast_mul , operator . mul , _internal . _mul_scalar , None )