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224 lines
6.8 KiB
224 lines
6.8 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore.common.initializer import initializer
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class Assign(nn.Cell):
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def __init__(self, x, y):
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super(Assign, self).__init__()
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self.x = Parameter(initializer(x, x.shape), name="x")
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self.y = Parameter(initializer(y, y.shape), name="y")
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self.assign = P.Assign()
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def construct(self):
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self.assign(self.y, self.x)
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return self.y
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_bool():
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x = Tensor(np.ones([3, 3]).astype(np.bool_))
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y = Tensor(np.zeros([3, 3]).astype(np.bool_))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.bool_)
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print(output)
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assert np.all(output == output_expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_int8():
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x = Tensor(np.ones([3, 3]).astype(np.int8))
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y = Tensor(np.zeros([3, 3]).astype(np.int8))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.int8)
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print(output)
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assert np.all(output == output_expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_uint8():
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x = Tensor(np.ones([3, 3]).astype(np.uint8))
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y = Tensor(np.zeros([3, 3]).astype(np.uint8))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.uint8)
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print(output)
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assert np.all(output == output_expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_int16():
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x = Tensor(np.ones([3, 3]).astype(np.int16))
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y = Tensor(np.zeros([3, 3]).astype(np.int16))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.int16)
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print(output)
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assert np.all(output == output_expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_uint16():
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x = Tensor(np.ones([3, 3]).astype(np.uint16))
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y = Tensor(np.zeros([3, 3]).astype(np.uint16))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.uint16)
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print(output)
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assert np.all(output == output_expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_int32():
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x = Tensor(np.ones([3, 3]).astype(np.int32))
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y = Tensor(np.zeros([3, 3]).astype(np.int32))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.int32)
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print(output)
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assert np.all(output == output_expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_uint32():
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x = Tensor(np.ones([3, 3]).astype(np.uint32))
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y = Tensor(np.zeros([3, 3]).astype(np.uint32))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.uint32)
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print(output)
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assert np.all(output == output_expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_int64():
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x = Tensor(np.ones([3, 3]).astype(np.int64))
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y = Tensor(np.zeros([3, 3]).astype(np.int64))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.int64)
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print(output)
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assert np.all(output == output_expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_uint64():
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x = Tensor(np.ones([3, 3]).astype(np.uint64))
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y = Tensor(np.zeros([3, 3]).astype(np.uint64))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.uint64)
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print(output)
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assert np.all(output == output_expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_float16():
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x = Tensor(np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float16))
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y = Tensor(np.array([[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8],
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[0.1, 0.2, 0.3]]).astype(np.float16))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float16)
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print(output)
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assert np.all(output - output_expect < 1e-6)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_float32():
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x = Tensor(np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float32))
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y = Tensor(np.array([[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8],
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[0.1, 0.2, 0.3]]).astype(np.float32))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float32)
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print(output)
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assert np.all(output - output_expect < 1e-6)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_assign_float64():
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x = Tensor(np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float64))
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y = Tensor(np.array([[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8],
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[0.1, 0.2, 0.3]]).astype(np.float64))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float64)
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print(output)
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assert np.all(output - output_expect < 1e-6)
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