elif reduction == 'sum':
        ret = 2
    else:
        ret = -1  # TODO: remove once JIT exceptions support control flow
        raise ValueError("{} is not a valid value for reduction".format(reduction))
    return ret

# In order to support previous versions, accept boolean size_average and reduce
# and convert them into the new constants for now

            

Reported by Pylint.

              from typing import Optional
import warnings

# NB: Keep this file in sync with enums in aten/src/ATen/core/Reduction.h


def get_enum(reduction: str) -> int:
    if reduction == 'none':
        ret = 0

            

Reported by Pylint.

              # NB: Keep this file in sync with enums in aten/src/ATen/core/Reduction.h


def get_enum(reduction: str) -> int:
    if reduction == 'none':
        ret = 0
    elif reduction == 'mean':
        ret = 1
    elif reduction == 'elementwise_mean':

            

Reported by Pylint.

                  elif reduction == 'mean':
        ret = 1
    elif reduction == 'elementwise_mean':
        warnings.warn("reduction='elementwise_mean' is deprecated, please use reduction='mean' instead.")
        ret = 1
    elif reduction == 'sum':
        ret = 2
    else:
        ret = -1  # TODO: remove once JIT exceptions support control flow

            

Reported by Pylint.

              

# We use these functions in torch/legacy as well, in which case we'll silence the warning
def legacy_get_string(size_average: Optional[bool], reduce: Optional[bool], emit_warning: bool = True) -> str:
    warning = "size_average and reduce args will be deprecated, please use reduction='{}' instead."

    if size_average is None:
        size_average = True
    if reduce is None:

            

Reported by Pylint.

              

# We use these functions in torch/legacy as well, in which case we'll silence the warning
def legacy_get_string(size_average: Optional[bool], reduce: Optional[bool], emit_warning: bool = True) -> str:
    warning = "size_average and reduce args will be deprecated, please use reduction='{}' instead."

    if size_average is None:
        size_average = True
    if reduce is None:

            

Reported by Pylint.

                  return ret


def legacy_get_enum(size_average: Optional[bool], reduce: Optional[bool], emit_warning: bool = True) -> int:
    return get_enum(legacy_get_string(size_average, reduce, emit_warning))

            

Reported by Pylint.

                  return ret


def legacy_get_enum(size_average: Optional[bool], reduce: Optional[bool], emit_warning: bool = True) -> int:
    return get_enum(legacy_get_string(size_average, reduce, emit_warning))

            

Reported by Pylint.

              



# make sure we use cpp implementation of protobuf
import os
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "cpp"
# then import protobuf
from caffe2.proto import caffe2_pb2, metanet_pb2

            

Reported by Pylint.

              import os
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "cpp"
# then import protobuf
from caffe2.proto import caffe2_pb2, metanet_pb2

import unittest


class TestCrossProtoCalls(unittest.TestCase):

            

Reported by Pylint.

              # then import protobuf
from caffe2.proto import caffe2_pb2, metanet_pb2

import unittest


class TestCrossProtoCalls(unittest.TestCase):
    def testSimple(self):
        net = caffe2_pb2.NetDef()

            

Reported by Pylint.

              # then import protobuf
from caffe2.proto import caffe2_pb2, metanet_pb2

import unittest


class TestCrossProtoCalls(unittest.TestCase):
    def testSimple(self):
        net = caffe2_pb2.NetDef()

            

Reported by Pylint.

              import unittest


class TestCrossProtoCalls(unittest.TestCase):
    def testSimple(self):
        net = caffe2_pb2.NetDef()
        meta = metanet_pb2.MetaNetDef()
        # if metanet_pb2 wasn't initialized properly the following fails with a
        # cryptic message: "Parameter to MergeFrom() must be instance of same

            

Reported by Pylint.

              

class TestCrossProtoCalls(unittest.TestCase):
    def testSimple(self):
        net = caffe2_pb2.NetDef()
        meta = metanet_pb2.MetaNetDef()
        # if metanet_pb2 wasn't initialized properly the following fails with a
        # cryptic message: "Parameter to MergeFrom() must be instance of same
        # class: expected caffe2.NetDef got caffe2.NetDef."

            

Reported by Pylint.

              

class TestCrossProtoCalls(unittest.TestCase):
    def testSimple(self):
        net = caffe2_pb2.NetDef()
        meta = metanet_pb2.MetaNetDef()
        # if metanet_pb2 wasn't initialized properly the following fails with a
        # cryptic message: "Parameter to MergeFrom() must be instance of same
        # class: expected caffe2.NetDef got caffe2.NetDef."

            

Reported by Pylint.

              

class TestCrossProtoCalls(unittest.TestCase):
    def testSimple(self):
        net = caffe2_pb2.NetDef()
        meta = metanet_pb2.MetaNetDef()
        # if metanet_pb2 wasn't initialized properly the following fails with a
        # cryptic message: "Parameter to MergeFrom() must be instance of same
        # class: expected caffe2.NetDef got caffe2.NetDef."

            

Reported by Pylint.

              from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np


class TestLengthsTileOp(serial.SerializedTestCase):


            

Reported by Pylint.

                          )
        ),
        **hu.gcs)
    def test_lengths_tile(self, inputs, gc, dc):
        data, lengths = inputs

        def lengths_tile_op(data, lengths):
            return [np.concatenate([
                [d] * l for d, l in zip(data, lengths)

            

Reported by Pylint.

              




from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st

            

Reported by Pylint.

              import numpy as np


class TestLengthsTileOp(serial.SerializedTestCase):

    @serial.given(
        inputs=st.integers(min_value=1, max_value=20).flatmap(
            lambda size: st.tuples(
                hu.arrays([size], dtype=np.float32),

            

Reported by Pylint.

                          )
        ),
        **hu.gcs)
    def test_lengths_tile(self, inputs, gc, dc):
        data, lengths = inputs

        def lengths_tile_op(data, lengths):
            return [np.concatenate([
                [d] * l for d, l in zip(data, lengths)

            

Reported by Pylint.

                          )
        ),
        **hu.gcs)
    def test_lengths_tile(self, inputs, gc, dc):
        data, lengths = inputs

        def lengths_tile_op(data, lengths):
            return [np.concatenate([
                [d] * l for d, l in zip(data, lengths)

            

Reported by Pylint.

                          )
        ),
        **hu.gcs)
    def test_lengths_tile(self, inputs, gc, dc):
        data, lengths = inputs

        def lengths_tile_op(data, lengths):
            return [np.concatenate([
                [d] * l for d, l in zip(data, lengths)

            

Reported by Pylint.

                              [d] * l for d, l in zip(data, lengths)
            ])]

        op = core.CreateOperator(
            "LengthsTile",
            ["data", "lengths"],
            ["output"],
        )


            

Reported by Pylint.

              import torch
from . import bar
# This file contains definitions of script classes.
# They are used by test_jit.py to test ScriptClass imports


@torch.jit.script  # noqa: B903
class FooSameName(object):
    def __init__(self, x):

            

Reported by Pylint.

              import torch
from . import bar
# This file contains definitions of script classes.
# They are used by test_jit.py to test ScriptClass imports


@torch.jit.script  # noqa: B903
class FooSameName(object):
    def __init__(self, x):

            

Reported by Pylint.

              import torch
from . import bar
# This file contains definitions of script classes.
# They are used by test_jit.py to test ScriptClass imports


@torch.jit.script  # noqa: B903
class FooSameName(object):
    def __init__(self, x):

            

Reported by Pylint.

              import torch
from . import bar
# This file contains definitions of script classes.
# They are used by test_jit.py to test ScriptClass imports


@torch.jit.script  # noqa: B903
class FooSameName(object):
    def __init__(self, x):

            

Reported by Pylint.

              

@torch.jit.script  # noqa: B903
class FooSameName(object):
    def __init__(self, x):
        self.x = x
        self.nested = bar.FooSameName(x)

            

Reported by Pylint.

              

@torch.jit.script  # noqa: B903
class FooSameName(object):
    def __init__(self, x):
        self.x = x
        self.nested = bar.FooSameName(x)

            

Reported by Pylint.

              

@torch.jit.script  # noqa: B903
class FooSameName(object):
    def __init__(self, x):
        self.x = x
        self.nested = bar.FooSameName(x)

            

Reported by Pylint.

              @torch.jit.script  # noqa: B903
class FooSameName(object):
    def __init__(self, x):
        self.x = x
        self.nested = bar.FooSameName(x)

            

Reported by Pylint.

              #
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from torch.distributed.pipeline.sync.pipeline import _clock_cycles


def test_clock_cycles():
    assert list(_clock_cycles(1, 1)) == [[(0, 0)]]
    assert list(_clock_cycles(1, 3)) == [[(0, 0)], [(0, 1)], [(0, 2)]]

            

Reported by Pylint.

              # Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from torch.distributed.pipeline.sync.pipeline import _clock_cycles



            

Reported by Pylint.

              from torch.distributed.pipeline.sync.pipeline import _clock_cycles


def test_clock_cycles():
    assert list(_clock_cycles(1, 1)) == [[(0, 0)]]
    assert list(_clock_cycles(1, 3)) == [[(0, 0)], [(0, 1)], [(0, 2)]]
    assert list(_clock_cycles(3, 1)) == [[(0, 0)], [(1, 0)], [(2, 0)]]

    assert list(_clock_cycles(3, 3)) == [

            

Reported by Pylint.

              

def test_clock_cycles():
    assert list(_clock_cycles(1, 1)) == [[(0, 0)]]
    assert list(_clock_cycles(1, 3)) == [[(0, 0)], [(0, 1)], [(0, 2)]]
    assert list(_clock_cycles(3, 1)) == [[(0, 0)], [(1, 0)], [(2, 0)]]

    assert list(_clock_cycles(3, 3)) == [
        [(0, 0)],

            

Reported by Bandit.

              
def test_clock_cycles():
    assert list(_clock_cycles(1, 1)) == [[(0, 0)]]
    assert list(_clock_cycles(1, 3)) == [[(0, 0)], [(0, 1)], [(0, 2)]]
    assert list(_clock_cycles(3, 1)) == [[(0, 0)], [(1, 0)], [(2, 0)]]

    assert list(_clock_cycles(3, 3)) == [
        [(0, 0)],
        [(1, 0), (0, 1)],

            

Reported by Bandit.

              def test_clock_cycles():
    assert list(_clock_cycles(1, 1)) == [[(0, 0)]]
    assert list(_clock_cycles(1, 3)) == [[(0, 0)], [(0, 1)], [(0, 2)]]
    assert list(_clock_cycles(3, 1)) == [[(0, 0)], [(1, 0)], [(2, 0)]]

    assert list(_clock_cycles(3, 3)) == [
        [(0, 0)],
        [(1, 0), (0, 1)],
        [(2, 0), (1, 1), (0, 2)],

            

Reported by Bandit.

                  assert list(_clock_cycles(1, 3)) == [[(0, 0)], [(0, 1)], [(0, 2)]]
    assert list(_clock_cycles(3, 1)) == [[(0, 0)], [(1, 0)], [(2, 0)]]

    assert list(_clock_cycles(3, 3)) == [
        [(0, 0)],
        [(1, 0), (0, 1)],
        [(2, 0), (1, 1), (0, 2)],
        [(2, 1), (1, 2)],
        [(2, 2)],

            

Reported by Bandit.

                      [(2, 2)],
    ]

    assert list(_clock_cycles(4, 2)) == [
        [(0, 0)],
        [(1, 0), (0, 1)],
        [(2, 0), (1, 1)],
        [(3, 0), (2, 1)],
        [(3, 1)],

            

Reported by Bandit.

              
import sys

import torch.distributed as dist

if not dist.is_available():
    print("Distributed not available, skipping tests", file=sys.stderr)
    sys.exit(0)


            

Reported by Pylint.

                  print("Distributed not available, skipping tests", file=sys.stderr)
    sys.exit(0)

from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed.rpc.tensorpipe_rpc_agent_test_fixture import (
    TensorPipeRpcAgentTestFixture,
)
from torch.testing._internal.distributed.rpc_utils import (
    GENERIC_CUDA_TESTS,

            

Reported by Pylint.

                  sys.exit(0)

from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed.rpc.tensorpipe_rpc_agent_test_fixture import (
    TensorPipeRpcAgentTestFixture,
)
from torch.testing._internal.distributed.rpc_utils import (
    GENERIC_CUDA_TESTS,
    TENSORPIPE_CUDA_TESTS,

            

Reported by Pylint.

              from torch.testing._internal.distributed.rpc.tensorpipe_rpc_agent_test_fixture import (
    TensorPipeRpcAgentTestFixture,
)
from torch.testing._internal.distributed.rpc_utils import (
    GENERIC_CUDA_TESTS,
    TENSORPIPE_CUDA_TESTS,
    MultiProcess,
    generate_tests,
)

            

Reported by Pylint.

              #!/usr/bin/env python3

import sys

import torch.distributed as dist

if not dist.is_available():
    print("Distributed not available, skipping tests", file=sys.stderr)
    sys.exit(0)

            

Reported by Pylint.

                  print("Distributed not available, skipping tests", file=sys.stderr)
    sys.exit(0)

from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed.rpc.tensorpipe_rpc_agent_test_fixture import (
    TensorPipeRpcAgentTestFixture,
)
from torch.testing._internal.distributed.rpc_utils import (
    GENERIC_CUDA_TESTS,

            

Reported by Pylint.

                  sys.exit(0)

from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed.rpc.tensorpipe_rpc_agent_test_fixture import (
    TensorPipeRpcAgentTestFixture,
)
from torch.testing._internal.distributed.rpc_utils import (
    GENERIC_CUDA_TESTS,
    TENSORPIPE_CUDA_TESTS,

            

Reported by Pylint.

              from torch.testing._internal.distributed.rpc.tensorpipe_rpc_agent_test_fixture import (
    TensorPipeRpcAgentTestFixture,
)
from torch.testing._internal.distributed.rpc_utils import (
    GENERIC_CUDA_TESTS,
    TENSORPIPE_CUDA_TESTS,
    MultiProcess,
    generate_tests,
)

            

Reported by Pylint.

                ASSERT_TRUE(resd.equal(refd));
}

TEST(LiteInterpreterTest, CheckAttrAccess) {
  Module m("m");
  m.register_attribute("mobile_optimized", BoolType::get(), true);

  std::stringstream ss;
  m._save_for_mobile(ss);

            

Reported by Cppcheck.

              }

TEST(LiteInterpreterTest, Conv) {
  auto s = std::getenv("PYTORCH_TEST_WITH_TSAN");
  if (s && strcmp(s, "1") == 0)
    return;

  std::vector<torch::jit::IValue> inputs;


            

Reported by FlawFinder.

              }

TEST(LiteInterpreterTest, DefaultArgsConv) {
  auto s = std::getenv("PYTORCH_TEST_WITH_TSAN");
  if (s && strcmp(s, "1") == 0)
    return;

  std::vector<torch::jit::IValue> inputs;


            

Reported by FlawFinder.

              
  auto resd = res.toTensor();
  auto refd = ref.toTensor();
  ASSERT_TRUE(resd.equal(refd));
}

TEST(LiteInterpreterTest, CheckAttrAccess) {
  Module m("m");
  m.register_attribute("mobile_optimized", BoolType::get(), true);

            

Reported by FlawFinder.

                  const std::vector<IValue>& actual_result_list,
    const std::vector<Tensor>& expect_result_list) {
  AT_ASSERT(actual_result_list.size() == expect_result_list.size());
  AT_ASSERT(actual_result_list[0].toTensor().equal(expect_result_list[0]));
  AT_ASSERT(
      actual_result_list[1].toTensor().dim() == expect_result_list[1].dim());
  AT_ASSERT(actual_result_list[2].toTensor().equal(expect_result_list[2]));
}


            

Reported by FlawFinder.

                AT_ASSERT(actual_result_list[0].toTensor().equal(expect_result_list[0]));
  AT_ASSERT(
      actual_result_list[1].toTensor().dim() == expect_result_list[1].dim());
  AT_ASSERT(actual_result_list[2].toTensor().equal(expect_result_list[2]));
}

void runAndCheckTorchScriptModel(
    std::stringstream& input_model_stream,
    const std::vector<IValue>& input_data,

            

Reported by FlawFinder.

                }
  auto output = res.toTensor();
  AT_ASSERT(outputref.dim() == output.dim());
  AT_ASSERT(output.equal(outputref));
}

namespace {
void testLiteModuleCompareResultTensors(
    Module& m,

            

Reported by FlawFinder.

                }
  auto output = res.toTensor();
  AT_ASSERT(outputref.dim() == output.dim());
  AT_ASSERT(output.equal(outputref));
}

void testDefaultArgsPinv(int num_args) {
  Module m("m");
  if (num_args == 1) {

            

Reported by FlawFinder.

              
import numpy as np

import caffe2.proto.caffe2_pb2 as caffe2_pb2
from caffe2.python import core, workspace, timeout_guard, test_util


class BlobsQueueDBTest(test_util.TestCase):
    def test_create_blobs_queue_db_string(self):

            

Reported by Pylint.

              




import numpy as np

import caffe2.proto.caffe2_pb2 as caffe2_pb2
from caffe2.python import core, workspace, timeout_guard, test_util

            

Reported by Pylint.

              from caffe2.python import core, workspace, timeout_guard, test_util


class BlobsQueueDBTest(test_util.TestCase):
    def test_create_blobs_queue_db_string(self):
        def add_blobs(queue, num_samples):
            blob = core.BlobReference("blob")
            status = core.BlobReference("blob_status")
            for i in range(num_samples):

            

Reported by Pylint.

              

class BlobsQueueDBTest(test_util.TestCase):
    def test_create_blobs_queue_db_string(self):
        def add_blobs(queue, num_samples):
            blob = core.BlobReference("blob")
            status = core.BlobReference("blob_status")
            for i in range(num_samples):
                self._add_blob_to_queue(

            

Reported by Pylint.

                              )
        self._test_create_blobs_queue_db(add_blobs)

    def test_create_blobs_queue_db_tensor(self):
        def add_blobs(queue, num_samples):
            blob = core.BlobReference("blob")
            status = core.BlobReference("blob_status")
            for i in range(num_samples):
                data = self._create_test_tensor_protos(i)

            

Reported by Pylint.

                              self.assertEqual(1, item)
        workspace.RunNetOnce(close_net)

    def _add_blob_to_queue(self, queue, data, blob, status):
        workspace.FeedBlob(blob, data)
        op = core.CreateOperator(
            "SafeEnqueueBlobs",
            [queue, blob],
            [blob, status],

            

Reported by Pylint.

              
    def _add_blob_to_queue(self, queue, data, blob, status):
        workspace.FeedBlob(blob, data)
        op = core.CreateOperator(
            "SafeEnqueueBlobs",
            [queue, blob],
            [blob, status],
        )
        workspace.RunOperatorOnce(op)

            

Reported by Pylint.

                      )
        workspace.RunOperatorOnce(op)

    def _create_test_tensor_protos(self, idx):
        item = caffe2_pb2.TensorProtos()
        data = item.protos.add()
        data.data_type = core.DataType.STRING
        data.string_data.append("foo{}".format(idx).encode('utf-8'))
        label = item.protos.add()

            

Reported by Pylint.

                constexpr size_t kMagicValueLength = 8;
  if (size > kMagicValueLength) {
    // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
    char buf[kMagicValueLength];
    read(0, buf, kMagicValueLength);
    valid("checking magic number");
    AT_ASSERTM(
        memcmp("PYTORCH1", buf, kMagicValueLength) != 0,
        "File is an unsupported archive format from the preview release.");

            

Reported by FlawFinder.

                mz_uint num_files = mz_zip_reader_get_num_files(ar_.get());
  std::vector<std::string> out;
  // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
  char buf[MZ_ZIP_MAX_ARCHIVE_FILENAME_SIZE];
  for (size_t i = 0; i < num_files; i++) {
    mz_zip_reader_get_filename(ar_.get(), i, buf, MZ_ZIP_MAX_ARCHIVE_FILENAME_SIZE);
    if (strncmp(
            buf,
            archive_name_plus_slash_.data(),

            

Reported by FlawFinder.

                  CAFFE_THROW("invalid file name: ", file_name);
  }
  if (!writer_func_) {
    file_stream_.open(
        file_name,
        std::ofstream::out | std::ofstream::trunc | std::ofstream::binary);
    valid("opening archive ", file_name.c_str());
    TORCH_CHECK(file_stream_, "File ", file_name, " cannot be opened.");
    writer_func_ = [this](const void* buf, size_t nbytes) -> size_t {

            

Reported by FlawFinder.

              
size_t istream_read_func(void *pOpaque, mz_uint64 file_ofs, void *pBuf, size_t n) {
  auto self = static_cast<PyTorchStreamReader*>(pOpaque);
  return self->read(file_ofs, static_cast<char*>(pBuf), n);
}

static std::string basename(const std::string& name) {
  size_t start = 0;
  for(size_t i = 0; i < name.size(); ++i) {

            

Reported by FlawFinder.

                return name.substr(start, end - start);
}

size_t PyTorchStreamReader::read(uint64_t pos, char* buf, size_t n) {
  return in_->read(pos, buf, n, "reading file");
}

// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
PyTorchStreamReader::PyTorchStreamReader(const std::string& file_name)

            

Reported by FlawFinder.

              }

size_t PyTorchStreamReader::read(uint64_t pos, char* buf, size_t n) {
  return in_->read(pos, buf, n, "reading file");
}

// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
PyTorchStreamReader::PyTorchStreamReader(const std::string& file_name)
    : ar_(std::make_unique<mz_zip_archive>()),

            

Reported by FlawFinder.

                if (size > kMagicValueLength) {
    // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
    char buf[kMagicValueLength];
    read(0, buf, kMagicValueLength);
    valid("checking magic number");
    AT_ASSERTM(
        memcmp("PYTORCH1", buf, kMagicValueLength) != 0,
        "File is an unsupported archive format from the preview release.");
  }

            

Reported by FlawFinder.

                valid("retrieving file meta-data for ", name.c_str());
  // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
  uint8_t local_header[MZ_ZIP_LOCAL_DIR_HEADER_SIZE];
  in_->read(
      stat.m_local_header_ofs,
      local_header,
      MZ_ZIP_LOCAL_DIR_HEADER_SIZE,
      "reading file header");
  size_t filename_len = read_le_16(local_header + MZ_ZIP_LDH_FILENAME_LEN_OFS);

            

Reported by FlawFinder.

              import os
import sys
from torch._C import parse_ir
from torch.testing import FileCheck

# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase

            

Reported by Pylint.

              import os
import sys
from torch._C import parse_ir
from torch.testing import FileCheck

# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase

            

Reported by Pylint.

              # Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase

if __name__ == '__main__':
    raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
                       "\tpython test/test_jit.py TESTNAME\n\n"
                       "instead.")

            

Reported by Pylint.

              import os
import sys
from torch._C import parse_ir
from torch.testing import FileCheck

# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase

            

Reported by Pylint.

              # Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase

if __name__ == '__main__':
    raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
                       "\tpython test/test_jit.py TESTNAME\n\n"
                       "instead.")

            

Reported by Pylint.

                                     "instead.")

# Tests that Python slice class is supported in TorchScript
class TestIgnorableArgs(JitTestCase):
    def test_slice_ignorable_args_for_slice(self):
        graph_str = """graph():
            %13 : int = prim::Constant[value=0]()
            %10 : bool = prim::Constant[value=0]()
            %8 : NoneType = prim::Constant()

            

Reported by Pylint.

                                     "instead.")

# Tests that Python slice class is supported in TorchScript
class TestIgnorableArgs(JitTestCase):
    def test_slice_ignorable_args_for_slice(self):
        graph_str = """graph():
            %13 : int = prim::Constant[value=0]()
            %10 : bool = prim::Constant[value=0]()
            %8 : NoneType = prim::Constant()

            

Reported by Pylint.

              
# Tests that Python slice class is supported in TorchScript
class TestIgnorableArgs(JitTestCase):
    def test_slice_ignorable_args_for_slice(self):
        graph_str = """graph():
            %13 : int = prim::Constant[value=0]()
            %10 : bool = prim::Constant[value=0]()
            %8 : NoneType = prim::Constant()
            %0 : int = prim::Constant[value=1]()

            

Reported by Pylint.