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233 lines
7.5 KiB
233 lines
7.5 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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import docstring_checker
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import pylint.testutils
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import astroid
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import pytest
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import sys
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class TestDocstring(pylint.testutils.CheckerTestCase):
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CHECKER_CLASS = docstring_checker.DocstringChecker
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def test_one_line(self):
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func_node = astroid.extract_node('''
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def test():
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"""get
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news.
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"""
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if True:
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return 5
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return 5
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''')
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self.checker.visit_functiondef(func_node)
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got = self.linter.release_messages()
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assert len(got) == 1
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assert 'W9001' == got[0][0]
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def test_one_line(self):
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func_node = astroid.extract_node('''
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def test():
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"""get news"""
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if True:
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return 5
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return 5
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''')
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self.checker.visit_functiondef(func_node)
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got = self.linter.release_messages()
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assert len(got) == 1
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assert 'W9002' == got[0][0]
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def test_args(self):
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func_node = astroid.extract_node('''
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def test(scale, mean):
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"""get news.
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Args:
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scale (int): scale is the number.
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"""
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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''')
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self.checker.visit_functiondef(func_node)
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got = self.linter.release_messages()
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assert len(got) == 1
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assert 'W9003' == got[0][0]
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def test_missing(self):
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func_node = astroid.extract_node('''
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def test():
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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''')
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self.checker.visit_functiondef(func_node)
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got = self.linter.release_messages()
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assert len(got) == 1
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assert 'W9005' == got[0][0]
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def test_indent(self):
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func_node = astroid.extract_node('''
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def test():
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""" get get get get get get get get
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get get get get get get get get.
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"""
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pass
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''')
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self.checker.visit_functiondef(func_node)
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got = self.linter.release_messages()
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assert len(got) == 1
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assert 'W9006' == got[0][0]
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def test_with_resturns(self):
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func_node = astroid.extract_node('''
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def test():
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"""get news.
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Args:
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scale (int): scale is the number.
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"""
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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return mean
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''')
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self.checker.visit_functiondef(func_node)
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got = self.linter.release_messages()
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assert len(got) == 1
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assert 'W9007' == got[0][0]
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def test_with_raises(self):
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func_node = astroid.extract_node('''
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def test():
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"""get news.
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Args:
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scale (int): scale is the number.
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"""
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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mean=scale
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raise ValueError('A very specific bad thing happened.')
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''')
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self.checker.visit_functiondef(func_node)
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got = self.linter.release_messages()
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assert len(got) == 1
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assert 'W9008' == got[0][0]
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def test_no_message(self):
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p = '''
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def fc(input,
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size,
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num_flatten_dims=1,
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param_attr=None,
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bias_attr=None,
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act=None,
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name=None):
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"""
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**Fully Connected Layer**
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The fully connected layer can take multiple tensors as its inputs. It
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creates a variable called weights for each input tensor, which represents
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a fully connected weight matrix from each input unit to each output unit.
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The fully connected layer multiplies each input tensor with its coresponding
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weight to produce an output Tensor. If multiple input tensors are given,
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the results of multiple multiplications will be sumed up. If bias_attr is
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not None, a bias variable will be created and added to the output. Finally,
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if activation is not None, it will be applied to the output as well.
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This process can be formulated as follows:
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Args:
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input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
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the input tensor(s) is at least 2.
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size(int): The number of output units in this layer.
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num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
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two dimensions. If this happens, the multidimensional tensor will first be flattened
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into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
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tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
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dimensions will be flatten to form the first dimension of the final matrix (height of
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the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
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form the second dimension of the final matrix (width of the matrix). For example, suppose
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`X` is a 6-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
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Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
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param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
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parameters/weights of this layer.
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bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
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of this layer. If it is set to None, no bias will be added to the output units.
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act (str, default None): Activation to be applied to the output of this layer.
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name (str, default None): The name of this layer.
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Returns:
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A tensor variable storing the transformation result.
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Raises:
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ValueError: If rank of the input tensor is less than 2.
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Examples:
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.. code-block:: python
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data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
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fc = fluid.layers.fc(input=data, size=1000, act="tanh")
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"""
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raise ValueError('A very specific bad thing happened.')
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size = 1
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size = 1
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size = 1
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size = 1
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size = 1
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size = 1
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size = 1
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size = 1
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size = 1
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size = 1
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size = 1
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size = 1
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size = 1
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return size
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'''
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func_node = astroid.extract_node(p)
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self.checker.visit_functiondef(func_node)
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got = self.linter.release_messages()
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assert len(got) == 0
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