text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  import numpy as np  # type: ignore
  
  import onnx
  from ..base import Base
  from . import expect
  
  
  class ReduceL1(Base):
  
      @staticmethod
      def export_do_not_keepdims():  # type: () -> None
          shape = [3, 2, 2]
          axes = [2]
          keepdims = 0
  
          node = onnx.helper.make_node(
              'ReduceL1',
              inputs=['data'],
              outputs=['reduced'],
              axes=axes,
              keepdims=keepdims
          )
  
          data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
          #print(data)
          #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
  
          reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
          #print(reduced)
          #[[3., 7.], [11., 15.], [19., 23.]]
  
          expect(node, inputs=[data], outputs=[reduced],
              name='test_reduce_l1_do_not_keepdims_example')
  
          np.random.seed(0)
          data = np.random.uniform(-10, 10, shape).astype(np.float32)
          reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
  
          expect(node, inputs=[data], outputs=[reduced],
              name='test_reduce_l1_do_not_keepdims_random')
  
      @staticmethod
      def export_keepdims():  # type: () -> None
          shape = [3, 2, 2]
          axes = [2]
          keepdims = 1
  
          node = onnx.helper.make_node(
              'ReduceL1',
              inputs=['data'],
              outputs=['reduced'],
              axes=axes,
              keepdims=keepdims
          )
  
          data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
          #print(data)
          #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
  
          reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
          #print(reduced)
          #[[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
  
          expect(node, inputs=[data], outputs=[reduced],
              name='test_reduce_l1_keep_dims_example')
  
          np.random.seed(0)
          data = np.random.uniform(-10, 10, shape).astype(np.float32)
          reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
  
          expect(node, inputs=[data], outputs=[reduced],
              name='test_reduce_l1_keep_dims_random')
  
      @staticmethod
      def export_default_axes_keepdims():  # type: () -> None
          shape = [3, 2, 2]
          axes = None
          keepdims = 1
  
          node = onnx.helper.make_node(
              'ReduceL1',
              inputs=['data'],
              outputs=['reduced'],
              keepdims=keepdims
          )
  
          data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
          #print(data)
          #[[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
  
          reduced = np.sum(a=np.abs(data), axis=axes, keepdims=keepdims == 1)
          #print(reduced)
          #[[[78.]]]
  
          expect(node, inputs=[data], outputs=[reduced],
              name='test_reduce_l1_default_axes_keepdims_example')
  
          np.random.seed(0)
          data = np.random.uniform(-10, 10, shape).astype(np.float32)
          reduced = np.sum(a=np.abs(data), axis=axes, keepdims=keepdims == 1)
  
          expect(node, inputs=[data], outputs=[reduced],
              name='test_reduce_l1_default_axes_keepdims_random')
  