parameters: (lhs, rhs)
return_statement: return _ufunc_helper(
          lhs,
          rhs,
          op.broadcast_sub,
          operator.sub,
          _internal._minus_scalar,
          None)
docstring: Returns element-wise difference of the input arrays with broadcasting.
  
      Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
      ``mx.nd.broadcast_minus(lhs, rhs)`` when shapes of lhs and rhs do not
      match. If lhs.shape == rhs.shape, this is equivalent to
      ``mx.nd.elemwise_sub(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 mxnet.ndarray.sparse.array
          First array to be subtracted.
      rhs : scalar or mxnet.ndarray.sparse.array
           Second array to be subtracted.
          If ``lhs.shape != rhs.shape``, they must be
          broadcastable to a common shape.__spec__
  
      Returns
      -------
      NDArray
          The element-wise difference of the input arrays.
  
      Examples
      --------
      >>> a = mx.nd.ones((2,3)).tostype('csr')
      >>> b = mx.nd.ones((2,3)).tostype('csr')
      >>> a.asnumpy()
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> b.asnumpy()
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> (a-b).asnumpy()
      array([[ 0.,  0.,  0.],
             [ 0.,  0.,  0.]], dtype=float32)
      >>> c = mx.nd.ones((2,3)).tostype('row_sparse')
      >>> d = mx.nd.ones((2,3)).tostype('row_sparse')
      >>> c.asnumpy()
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> d.asnumpy()
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> (c-d).asnumpy()
      array([[ 0.,  0.,  0.],
             [ 0.,  0.,  0.]], dtype=float32)
docstring_summary: Returns element-wise difference of the input arrays with broadcasting.
docstring_tokens: Returns element - wise difference of the input arrays with broadcasting .
function: def subtract(lhs, rhs):
      """Returns element-wise difference of the input arrays with broadcasting.
  
      Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and
      ``mx.nd.broadcast_minus(lhs, rhs)`` when shapes of lhs and rhs do not
      match. If lhs.shape == rhs.shape, this is equivalent to
      ``mx.nd.elemwise_sub(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 mxnet.ndarray.sparse.array
          First array to be subtracted.
      rhs : scalar or mxnet.ndarray.sparse.array
           Second array to be subtracted.
          If ``lhs.shape != rhs.shape``, they must be
          broadcastable to a common shape.__spec__
  
      Returns
      -------
      NDArray
          The element-wise difference of the input arrays.
  
      Examples
      --------
      >>> a = mx.nd.ones((2,3)).tostype('csr')
      >>> b = mx.nd.ones((2,3)).tostype('csr')
      >>> a.asnumpy()
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> b.asnumpy()
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> (a-b).asnumpy()
      array([[ 0.,  0.,  0.],
             [ 0.,  0.,  0.]], dtype=float32)
      >>> c = mx.nd.ones((2,3)).tostype('row_sparse')
      >>> d = mx.nd.ones((2,3)).tostype('row_sparse')
      >>> c.asnumpy()
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> d.asnumpy()
      array([[ 1.,  1.,  1.],
             [ 1.,  1.,  1.]], dtype=float32)
      >>> (c-d).asnumpy()
      array([[ 0.,  0.,  0.],
             [ 0.,  0.,  0.]], dtype=float32)
      """
      # pylint: disable= no-member, protected-access
      if isinstance(lhs, NDArray) and isinstance(rhs, NDArray) and lhs.shape == rhs.shape:
          return _ufunc_helper(
              lhs,
              rhs,
              op.elemwise_sub,
              operator.sub,
              _internal._minus_scalar,
              None)
  
      return _ufunc_helper(
          lhs,
          rhs,
          op.broadcast_sub,
          operator.sub,
          _internal._minus_scalar,
          None)
function_tokens: def subtract ( lhs , rhs ) : # pylint: disable= no-member, protected-access if isinstance ( lhs , NDArray ) and isinstance ( rhs , NDArray ) and lhs . shape == rhs . shape : return _ufunc_helper ( lhs , rhs , op . elemwise_sub , operator . sub , _internal . _minus_scalar , None ) return _ufunc_helper ( lhs , rhs , op . broadcast_sub , operator . sub , _internal . _minus_scalar , None )