import unittest
import functools
from copy import deepcopy
import torch
from torch._six import inf
import torch.optim as optim
import torch.optim._multi_tensor as optim_mt
import torch.nn.functional as F
from torch.optim import SGD

            

Reported by Pylint.

              import functools
from copy import deepcopy
import torch
from torch._six import inf
import torch.optim as optim
import torch.optim._multi_tensor as optim_mt
import torch.nn.functional as F
from torch.optim import SGD
from torch.autograd import Variable

            

Reported by Pylint.

              from copy import deepcopy
import torch
from torch._six import inf
import torch.optim as optim
import torch.optim._multi_tensor as optim_mt
import torch.nn.functional as F
from torch.optim import SGD
from torch.autograd import Variable
from torch import sparse

            

Reported by Pylint.

              import torch
from torch._six import inf
import torch.optim as optim
import torch.optim._multi_tensor as optim_mt
import torch.nn.functional as F
from torch.optim import SGD
from torch.autograd import Variable
from torch import sparse
from torch.optim.lr_scheduler import LambdaLR, MultiplicativeLR, StepLR, \

            

Reported by Pylint.

              from torch._six import inf
import torch.optim as optim
import torch.optim._multi_tensor as optim_mt
import torch.nn.functional as F
from torch.optim import SGD
from torch.autograd import Variable
from torch import sparse
from torch.optim.lr_scheduler import LambdaLR, MultiplicativeLR, StepLR, \
    MultiStepLR, WarmUpLR, ExponentialLR, CosineAnnealingLR, ReduceLROnPlateau, \

            

Reported by Pylint.

              import torch.optim as optim
import torch.optim._multi_tensor as optim_mt
import torch.nn.functional as F
from torch.optim import SGD
from torch.autograd import Variable
from torch import sparse
from torch.optim.lr_scheduler import LambdaLR, MultiplicativeLR, StepLR, \
    MultiStepLR, WarmUpLR, ExponentialLR, CosineAnnealingLR, ReduceLROnPlateau, \
    _LRScheduler, CyclicLR, CosineAnnealingWarmRestarts, OneCycleLR

            

Reported by Pylint.

              import torch.optim._multi_tensor as optim_mt
import torch.nn.functional as F
from torch.optim import SGD
from torch.autograd import Variable
from torch import sparse
from torch.optim.lr_scheduler import LambdaLR, MultiplicativeLR, StepLR, \
    MultiStepLR, WarmUpLR, ExponentialLR, CosineAnnealingLR, ReduceLROnPlateau, \
    _LRScheduler, CyclicLR, CosineAnnealingWarmRestarts, OneCycleLR
from torch.optim.swa_utils import AveragedModel, SWALR, update_bn

            

Reported by Pylint.

              import torch.nn.functional as F
from torch.optim import SGD
from torch.autograd import Variable
from torch import sparse
from torch.optim.lr_scheduler import LambdaLR, MultiplicativeLR, StepLR, \
    MultiStepLR, WarmUpLR, ExponentialLR, CosineAnnealingLR, ReduceLROnPlateau, \
    _LRScheduler, CyclicLR, CosineAnnealingWarmRestarts, OneCycleLR
from torch.optim.swa_utils import AveragedModel, SWALR, update_bn
from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_UBSAN, load_tests, \

            

Reported by Pylint.

              from torch.optim import SGD
from torch.autograd import Variable
from torch import sparse
from torch.optim.lr_scheduler import LambdaLR, MultiplicativeLR, StepLR, \
    MultiStepLR, WarmUpLR, ExponentialLR, CosineAnnealingLR, ReduceLROnPlateau, \
    _LRScheduler, CyclicLR, CosineAnnealingWarmRestarts, OneCycleLR
from torch.optim.swa_utils import AveragedModel, SWALR, update_bn
from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_UBSAN, load_tests, \
    skipIfRocm

            

Reported by Pylint.

              from torch.optim.lr_scheduler import LambdaLR, MultiplicativeLR, StepLR, \
    MultiStepLR, WarmUpLR, ExponentialLR, CosineAnnealingLR, ReduceLROnPlateau, \
    _LRScheduler, CyclicLR, CosineAnnealingWarmRestarts, OneCycleLR
from torch.optim.swa_utils import AveragedModel, SWALR, update_bn
from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_UBSAN, load_tests, \
    skipIfRocm

# load_tests from common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings

            

Reported by Pylint.

              import unittest
from typing import Callable, Dict, Union, List, Optional

import torch
import torch.fx.experimental.optimization as optimization
from torch.fx._symbolic_trace import symbolic_trace
from torch.fx.experimental import graph_manipulation
from torch.fx.experimental import merge_matmul
from torch.fx.experimental.accelerator_partitioner import Partitioner

            

Reported by Pylint.

              from typing import Callable, Dict, Union, List, Optional

import torch
import torch.fx.experimental.optimization as optimization
from torch.fx._symbolic_trace import symbolic_trace
from torch.fx.experimental import graph_manipulation
from torch.fx.experimental import merge_matmul
from torch.fx.experimental.accelerator_partitioner import Partitioner
from torch.fx.experimental.normalize import NormalizeOperators, NormalizeArgs

            

Reported by Pylint.

              
import torch
import torch.fx.experimental.optimization as optimization
from torch.fx._symbolic_trace import symbolic_trace
from torch.fx.experimental import graph_manipulation
from torch.fx.experimental import merge_matmul
from torch.fx.experimental.accelerator_partitioner import Partitioner
from torch.fx.experimental.normalize import NormalizeOperators, NormalizeArgs
from torch.fx.experimental.param_fetch import lift_lowering_attrs_to_nodes

            

Reported by Pylint.

              import torch
import torch.fx.experimental.optimization as optimization
from torch.fx._symbolic_trace import symbolic_trace
from torch.fx.experimental import graph_manipulation
from torch.fx.experimental import merge_matmul
from torch.fx.experimental.accelerator_partitioner import Partitioner
from torch.fx.experimental.normalize import NormalizeOperators, NormalizeArgs
from torch.fx.experimental.param_fetch import lift_lowering_attrs_to_nodes
from torch.fx.experimental.partitioner_utils import (

            

Reported by Pylint.

              import torch.fx.experimental.optimization as optimization
from torch.fx._symbolic_trace import symbolic_trace
from torch.fx.experimental import graph_manipulation
from torch.fx.experimental import merge_matmul
from torch.fx.experimental.accelerator_partitioner import Partitioner
from torch.fx.experimental.normalize import NormalizeOperators, NormalizeArgs
from torch.fx.experimental.param_fetch import lift_lowering_attrs_to_nodes
from torch.fx.experimental.partitioner_utils import (
    NodeLatency,

            

Reported by Pylint.

              from torch.fx._symbolic_trace import symbolic_trace
from torch.fx.experimental import graph_manipulation
from torch.fx.experimental import merge_matmul
from torch.fx.experimental.accelerator_partitioner import Partitioner
from torch.fx.experimental.normalize import NormalizeOperators, NormalizeArgs
from torch.fx.experimental.param_fetch import lift_lowering_attrs_to_nodes
from torch.fx.experimental.partitioner_utils import (
    NodeLatency,
    get_partition_to_latency_mapping,

            

Reported by Pylint.

              from torch.fx.experimental import graph_manipulation
from torch.fx.experimental import merge_matmul
from torch.fx.experimental.accelerator_partitioner import Partitioner
from torch.fx.experimental.normalize import NormalizeOperators, NormalizeArgs
from torch.fx.experimental.param_fetch import lift_lowering_attrs_to_nodes
from torch.fx.experimental.partitioner_utils import (
    NodeLatency,
    get_partition_to_latency_mapping,
    get_latency_of_partitioned_graph,

            

Reported by Pylint.

              from torch.fx.experimental import merge_matmul
from torch.fx.experimental.accelerator_partitioner import Partitioner
from torch.fx.experimental.normalize import NormalizeOperators, NormalizeArgs
from torch.fx.experimental.param_fetch import lift_lowering_attrs_to_nodes
from torch.fx.experimental.partitioner_utils import (
    NodeLatency,
    get_partition_to_latency_mapping,
    get_latency_of_partitioned_graph,
    Device,

            

Reported by Pylint.

              from torch.fx.experimental.accelerator_partitioner import Partitioner
from torch.fx.experimental.normalize import NormalizeOperators, NormalizeArgs
from torch.fx.experimental.param_fetch import lift_lowering_attrs_to_nodes
from torch.fx.experimental.partitioner_utils import (
    NodeLatency,
    get_partition_to_latency_mapping,
    get_latency_of_partitioned_graph,
    Device,
    PartitionerConfig,

            

Reported by Pylint.

                  PartitionerConfig,
    PartitionMode,
)
from torch.fx.experimental.rewriter import RewritingTracer
from torch.fx.experimental.schema_type_annotation import AnnotateTypesWithSchema
from torch.fx.graph_module import GraphModule
from torch.fx.node import Node
from torch.fx.operator_schemas import (
    _torchscript_type_to_python_type,

            

Reported by Pylint.

              import random
import sys

from torch import Tensor
from torch.testing._internal.common_utils import TemporaryFileName
import torch

# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))

            

Reported by Pylint.

              import sys

from torch import Tensor
from torch.testing._internal.common_utils import TemporaryFileName
import torch

# 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)

            

Reported by Pylint.

              
from torch import Tensor
from torch.testing._internal.common_utils import TemporaryFileName
import torch

# 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,
                                               clear_class_registry)

if __name__ == "__main__":
    raise RuntimeError(
        "This test file is not meant to be run directly, use:\n\n"

            

Reported by Pylint.

                      """
        class Foo(torch.nn.Module):
            def __init__(self):
                super(Foo, self).__init__()
                self.foo = torch.nn.Linear(2, 2)
                self.bar = torch.nn.Linear(2, 2)

            def forward(self, x):
                x = self.foo(x)

            

Reported by Pylint.

              
        clear_class_registry()

        class Foo(torch.nn.Module):
            def __init__(self):
                super(Foo, self).__init__()
                self.foo = torch.nn.Linear(2, 2)

            def forward(self, x):

            

Reported by Pylint.

              
        clear_class_registry()

        def lol(x):  # noqa: F811
            return "hello"

        class Foo(torch.nn.Module):
            def forward(self, x):
                return lol(x)

            

Reported by Pylint.

                      def lol(x):  # noqa: F811
            return "hello"

        class Foo(torch.nn.Module):
            def forward(self, x):
                return lol(x)

        second_script_module = torch.jit.script(Foo())
        second_saved_module = io.BytesIO()

            

Reported by Pylint.

                              self.interface = ImplementInterface()

            def forward(self, x):
                return self.interface.bar(x)

        first_script_module = torch.jit.script(Foo())
        first_saved_module = io.BytesIO()
        torch.jit.save(first_script_module, first_saved_module)
        first_saved_module.seek(0)

            

Reported by Pylint.

                      clear_class_registry()

        @torch.jit.interface
        class MyInterface(object):
            def not_bar(self, x: Tensor) -> Tensor:
                pass

        @torch.jit.script  # noqa: F811
        class ImplementInterface(object):  # noqa: F811

            

Reported by Pylint.

              # TODO: enable linting check for this file

from typing import List, Any
import torch
import torch.nn as nn
import os
import sys
from torch import Tensor
from torch.testing._internal.jit_utils import JitTestCase, make_global

            

Reported by Pylint.

              
from typing import List, Any
import torch
import torch.nn as nn
import os
import sys
from torch import Tensor
from torch.testing._internal.jit_utils import JitTestCase, make_global


            

Reported by Pylint.

              import torch.nn as nn
import os
import sys
from torch import Tensor
from torch.testing._internal.jit_utils import JitTestCase, make_global

# 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)

            

Reported by Pylint.

              import os
import sys
from torch import Tensor
from torch.testing._internal.jit_utils import JitTestCase, make_global

# 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, execWrapper

            

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, execWrapper

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.

              # flake8: noqa
# TODO: enable linting check for this file

from typing import List, Any
import torch
import torch.nn as nn
import os
import sys
from torch import Tensor

            

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, execWrapper

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.

              # 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, execWrapper

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.")

class OrigModule(nn.Module):
    def __init__(self):
        super(OrigModule, self).__init__()

    def one(self, inp1: Tensor, inp2: Tensor) -> Tensor:
        return inp1 + inp2 + 1


            

Reported by Pylint.

                  def one(self, inp1: Tensor, inp2: Tensor) -> Tensor:
        return inp1 + inp2 + 1

    def two(self, input: Tensor) -> Tensor:
        return input + 2

    def forward(self, input: Tensor) -> Tensor:
        return input + self.one(input, input) + 1


            

Reported by Pylint.

              import unittest
import torch
from torch.fx import symbolic_trace
from torch.fx.experimental.unify_refinements import infer_symbolic_types
from torch.fx.experimental.refinement_types import Equality
from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
from torch.fx.annotate import annotate
from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
from torch.fx.experimental.rewriter import RewritingTracer

            

Reported by Pylint.

              import unittest
import torch
from torch.fx import symbolic_trace
from torch.fx.experimental.unify_refinements import infer_symbolic_types
from torch.fx.experimental.refinement_types import Equality
from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
from torch.fx.annotate import annotate
from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
from torch.fx.experimental.rewriter import RewritingTracer

            

Reported by Pylint.

              import unittest
import torch
from torch.fx import symbolic_trace
from torch.fx.experimental.unify_refinements import infer_symbolic_types
from torch.fx.experimental.refinement_types import Equality
from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
from torch.fx.annotate import annotate
from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
from torch.fx.experimental.rewriter import RewritingTracer

            

Reported by Pylint.

              import torch
from torch.fx import symbolic_trace
from torch.fx.experimental.unify_refinements import infer_symbolic_types
from torch.fx.experimental.refinement_types import Equality
from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
from torch.fx.annotate import annotate
from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
from torch.fx.experimental.rewriter import RewritingTracer
from torch.fx import GraphModule

            

Reported by Pylint.

              from torch.fx import symbolic_trace
from torch.fx.experimental.unify_refinements import infer_symbolic_types
from torch.fx.experimental.refinement_types import Equality
from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
from torch.fx.annotate import annotate
from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
from torch.fx.experimental.rewriter import RewritingTracer
from torch.fx import GraphModule
from torch.fx.passes.shape_prop import ShapeProp

            

Reported by Pylint.

              from torch.fx.experimental.unify_refinements import infer_symbolic_types
from torch.fx.experimental.refinement_types import Equality
from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
from torch.fx.annotate import annotate
from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
from torch.fx.experimental.rewriter import RewritingTracer
from torch.fx import GraphModule
from torch.fx.passes.shape_prop import ShapeProp
from torch.fx.experimental.unification import Var

            

Reported by Pylint.

              from torch.fx.experimental.refinement_types import Equality
from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
from torch.fx.annotate import annotate
from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
from torch.fx.experimental.rewriter import RewritingTracer
from torch.fx import GraphModule
from torch.fx.passes.shape_prop import ShapeProp
from torch.fx.experimental.unification import Var


            

Reported by Pylint.

              from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
from torch.fx.annotate import annotate
from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
from torch.fx.experimental.rewriter import RewritingTracer
from torch.fx import GraphModule
from torch.fx.passes.shape_prop import ShapeProp
from torch.fx.experimental.unification import Var

try:

            

Reported by Pylint.

              from torch.fx.annotate import annotate
from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
from torch.fx.experimental.rewriter import RewritingTracer
from torch.fx import GraphModule
from torch.fx.passes.shape_prop import ShapeProp
from torch.fx.experimental.unification import Var

try:
    from torchvision.models import resnet50

            

Reported by Pylint.

              from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
from torch.fx.experimental.rewriter import RewritingTracer
from torch.fx import GraphModule
from torch.fx.passes.shape_prop import ShapeProp
from torch.fx.experimental.unification import Var

try:
    from torchvision.models import resnet50


            

Reported by Pylint.

                      dtype = 6  # float
    else:
        dtype = sym_help.scalar_type_to_onnx.index(sym_help.cast_pytorch_to_onnx[dtype])
    min_val = g.op("Constant", value_t=torch.tensor(min_val, dtype=sym_help.scalar_type_to_pytorch_type[dtype]))
    max_val = g.op("Constant", value_t=torch.tensor(max_val, dtype=sym_help.scalar_type_to_pytorch_type[dtype]))
    return g.op("Clip", self, min_val, max_val)


def clamp(g, self, min, max):

            

Reported by Pylint.

                  else:
        dtype = sym_help.scalar_type_to_onnx.index(sym_help.cast_pytorch_to_onnx[dtype])
    min_val = g.op("Constant", value_t=torch.tensor(min_val, dtype=sym_help.scalar_type_to_pytorch_type[dtype]))
    max_val = g.op("Constant", value_t=torch.tensor(max_val, dtype=sym_help.scalar_type_to_pytorch_type[dtype]))
    return g.op("Clip", self, min_val, max_val)


def clamp(g, self, min, max):
    dtype = self.type().scalarType()

            

Reported by Pylint.

                      dtype = 6  # float
    else:
        dtype = sym_help.scalar_type_to_onnx.index(sym_help.cast_pytorch_to_onnx[dtype])
    min_val = g.op("Constant", value_t=torch.tensor(0, dtype=sym_help.scalar_type_to_pytorch_type[dtype]))
    max_val = g.op("Constant", value_t=torch.tensor(6, dtype=sym_help.scalar_type_to_pytorch_type[dtype]))
    return clamp(g, relu, min_val, max_val)


# Opset 11 gather accepts negative indices

            

Reported by Pylint.

                  else:
        dtype = sym_help.scalar_type_to_onnx.index(sym_help.cast_pytorch_to_onnx[dtype])
    min_val = g.op("Constant", value_t=torch.tensor(0, dtype=sym_help.scalar_type_to_pytorch_type[dtype]))
    max_val = g.op("Constant", value_t=torch.tensor(6, dtype=sym_help.scalar_type_to_pytorch_type[dtype]))
    return clamp(g, relu, min_val, max_val)


# Opset 11 gather accepts negative indices
@parse_args("v", "i", "v")

            

Reported by Pylint.

                  dtype = sym_help.scalar_type_to_pytorch_type[dtype]

    if accumulate:
        zeros = g.op("ConstantOfShape", g.op("Shape", self), value_t=torch.tensor([0], dtype=dtype))
        result = g.op("ScatterND", zeros, index, values)
        result = add(g, self, result)
    else:
        result = g.op("ScatterND", self, index, values)


            

Reported by Pylint.

              
@parse_args("v", "i", "none")
def cumsum(g, self, dim, dtype=None):
    dim_tensor = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.int))
    if dtype and dtype.node().kind() != "prim::Constant":
        parsed_dtype = sym_help._get_const(dtype, "i", "dtype")
        cast = g.op("Cast", self, to_i=sym_help.scalar_type_to_onnx[parsed_dtype])
    else:
        cast = self

            

Reported by Pylint.

              
@parse_args("v", "i", "none")
def cumsum(g, self, dim, dtype=None):
    dim_tensor = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.int))
    if dtype and dtype.node().kind() != "prim::Constant":
        parsed_dtype = sym_help._get_const(dtype, "i", "dtype")
        cast = g.op("Cast", self, to_i=sym_help.scalar_type_to_onnx[parsed_dtype])
    else:
        cast = self

            

Reported by Pylint.

                  # NOTE: source can have more elements than needed.
    # It could also have arbitrary shape.
    # This is not supported by ONNX::ScatterND, so we need to flatten and slice source tensor.
    source = view(g, source, torch.LongTensor([-1]))
    source = sym_help._slice_helper(g, source,
                                    axes=torch.LongTensor([0]),
                                    starts=torch.LongTensor([0]),
                                    ends=size(g, index, torch.LongTensor([0])),
                                    dynamic_slice=True)

            

Reported by Pylint.

                  # This is not supported by ONNX::ScatterND, so we need to flatten and slice source tensor.
    source = view(g, source, torch.LongTensor([-1]))
    source = sym_help._slice_helper(g, source,
                                    axes=torch.LongTensor([0]),
                                    starts=torch.LongTensor([0]),
                                    ends=size(g, index, torch.LongTensor([0])),
                                    dynamic_slice=True)
    return g.op("ScatterND", self, index, source)


            

Reported by Pylint.

                  source = view(g, source, torch.LongTensor([-1]))
    source = sym_help._slice_helper(g, source,
                                    axes=torch.LongTensor([0]),
                                    starts=torch.LongTensor([0]),
                                    ends=size(g, index, torch.LongTensor([0])),
                                    dynamic_slice=True)
    return g.op("ScatterND", self, index, source)



            

Reported by Pylint.

                  if not (cpu_device or ideep):
        for gpu in devices:
            if gpu >= workspace.NumGpuDevices():
                log.warning("** Only {} GPUs available, GPUs {} requested".format(
                    workspace.NumGpuDevices(), devices))
                break
        model_helper_obj._device_type = workspace.GpuDeviceType
        model_helper_obj._device_prefix = "gpu"
        model_helper_obj._shared_model = False

            

Reported by Pylint.

                              log.warning("** Only {} GPUs available, GPUs {} requested".format(
                    workspace.NumGpuDevices(), devices))
                break
        model_helper_obj._device_type = workspace.GpuDeviceType
        model_helper_obj._device_prefix = "gpu"
        model_helper_obj._shared_model = False
        device_name = "GPU"
        assert shared_model is False, "Shared model only supported on CPU"
    elif ideep:

            

Reported by Pylint.

                                  workspace.NumGpuDevices(), devices))
                break
        model_helper_obj._device_type = workspace.GpuDeviceType
        model_helper_obj._device_prefix = "gpu"
        model_helper_obj._shared_model = False
        device_name = "GPU"
        assert shared_model is False, "Shared model only supported on CPU"
    elif ideep:
        model_helper_obj._device_type = caffe2_pb2.IDEEP

            

Reported by Pylint.

                              break
        model_helper_obj._device_type = workspace.GpuDeviceType
        model_helper_obj._device_prefix = "gpu"
        model_helper_obj._shared_model = False
        device_name = "GPU"
        assert shared_model is False, "Shared model only supported on CPU"
    elif ideep:
        model_helper_obj._device_type = caffe2_pb2.IDEEP
        model_helper_obj._device_prefix = "ideep"

            

Reported by Pylint.

                      device_name = "GPU"
        assert shared_model is False, "Shared model only supported on CPU"
    elif ideep:
        model_helper_obj._device_type = caffe2_pb2.IDEEP
        model_helper_obj._device_prefix = "ideep"
        device_name = "IDEEP"
        model_helper_obj._shared_model = shared_model
        if shared_model and rendezvous is not None:
            assert "Shared model only supported on single-node currently"

            

Reported by Pylint.

                      assert shared_model is False, "Shared model only supported on CPU"
    elif ideep:
        model_helper_obj._device_type = caffe2_pb2.IDEEP
        model_helper_obj._device_prefix = "ideep"
        device_name = "IDEEP"
        model_helper_obj._shared_model = shared_model
        if shared_model and rendezvous is not None:
            assert "Shared model only supported on single-node currently"
    else:

            

Reported by Pylint.

                      model_helper_obj._device_type = caffe2_pb2.IDEEP
        model_helper_obj._device_prefix = "ideep"
        device_name = "IDEEP"
        model_helper_obj._shared_model = shared_model
        if shared_model and rendezvous is not None:
            assert "Shared model only supported on single-node currently"
    else:
        model_helper_obj._device_type = caffe2_pb2.CPU
        model_helper_obj._device_prefix = "cpu"

            

Reported by Pylint.

                      device_name = "IDEEP"
        model_helper_obj._shared_model = shared_model
        if shared_model and rendezvous is not None:
            assert "Shared model only supported on single-node currently"
    else:
        model_helper_obj._device_type = caffe2_pb2.CPU
        model_helper_obj._device_prefix = "cpu"
        device_name = "CPU"
        model_helper_obj._shared_model = shared_model

            

Reported by Pylint.

                      if shared_model and rendezvous is not None:
            assert "Shared model only supported on single-node currently"
    else:
        model_helper_obj._device_type = caffe2_pb2.CPU
        model_helper_obj._device_prefix = "cpu"
        device_name = "CPU"
        model_helper_obj._shared_model = shared_model
        if shared_model and rendezvous is not None:
            assert "Shared model only supported on single-node currently"

            

Reported by Pylint.

                          assert "Shared model only supported on single-node currently"
    else:
        model_helper_obj._device_type = caffe2_pb2.CPU
        model_helper_obj._device_prefix = "cpu"
        device_name = "CPU"
        model_helper_obj._shared_model = shared_model
        if shared_model and rendezvous is not None:
            assert "Shared model only supported on single-node currently"


            

Reported by Pylint.

              import unittest
import os

import torch

from torch.testing._internal.common_utils import run_tests, ProfilingMode, GRAPH_EXECUTOR
from torch.testing._internal.codegen.random_topo_test import runDefaultTestWithSeed

from test_jit import JitTestCase, RUN_CUDA

            

Reported by Pylint.

              
import torch

from torch.testing._internal.common_utils import run_tests, ProfilingMode, GRAPH_EXECUTOR
from torch.testing._internal.codegen.random_topo_test import runDefaultTestWithSeed

from test_jit import JitTestCase, RUN_CUDA

from jit.test_fuser_common import TestFuserCommon  # noqa: F401

            

Reported by Pylint.

              import torch

from torch.testing._internal.common_utils import run_tests, ProfilingMode, GRAPH_EXECUTOR
from torch.testing._internal.codegen.random_topo_test import runDefaultTestWithSeed

from test_jit import JitTestCase, RUN_CUDA

from jit.test_fuser_common import TestFuserCommon  # noqa: F401


            

Reported by Pylint.

              from torch.testing._internal.common_utils import run_tests, ProfilingMode, GRAPH_EXECUTOR
from torch.testing._internal.codegen.random_topo_test import runDefaultTestWithSeed

from test_jit import JitTestCase, RUN_CUDA

from jit.test_fuser_common import TestFuserCommon  # noqa: F401

import itertools
import numpy as np

            

Reported by Pylint.

              
from test_jit import JitTestCase, RUN_CUDA

from jit.test_fuser_common import TestFuserCommon  # noqa: F401

import itertools
import numpy as np

os.environ['PYTORCH_CUDA_FUSER_DISABLE_FALLBACK'] = '1'

            

Reported by Pylint.

              os.environ['PYTORCH_CUDA_FUSER_JIT_OPT_LEVEL'] = '0'

if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
    torch._C._jit_set_texpr_fuser_enabled(False)
    torch._C._jit_set_profiling_executor(True)
    torch._C._jit_set_profiling_mode(True)

FUSION_GROUP = 'prim::CudaFusionGroup'
FUSION_GUARD = 'prim::CudaFusionGuard'

            

Reported by Pylint.

              os.environ['PYTORCH_CUDA_FUSER_JIT_OPT_LEVEL'] = '0'

if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
    torch._C._jit_set_texpr_fuser_enabled(False)
    torch._C._jit_set_profiling_executor(True)
    torch._C._jit_set_profiling_mode(True)

FUSION_GROUP = 'prim::CudaFusionGroup'
FUSION_GUARD = 'prim::CudaFusionGuard'

            

Reported by Pylint.

              
if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
    torch._C._jit_set_texpr_fuser_enabled(False)
    torch._C._jit_set_profiling_executor(True)
    torch._C._jit_set_profiling_mode(True)

FUSION_GROUP = 'prim::CudaFusionGroup'
FUSION_GUARD = 'prim::CudaFusionGuard'


            

Reported by Pylint.

              
if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
    torch._C._jit_set_texpr_fuser_enabled(False)
    torch._C._jit_set_profiling_executor(True)
    torch._C._jit_set_profiling_mode(True)

FUSION_GROUP = 'prim::CudaFusionGroup'
FUSION_GUARD = 'prim::CudaFusionGuard'


            

Reported by Pylint.

              if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
    torch._C._jit_set_texpr_fuser_enabled(False)
    torch._C._jit_set_profiling_executor(True)
    torch._C._jit_set_profiling_mode(True)

FUSION_GROUP = 'prim::CudaFusionGroup'
FUSION_GUARD = 'prim::CudaFusionGuard'

class TestCudaFuser(JitTestCase):

            

Reported by Pylint.

                  default_symmetric_fixed_qparams_fake_quant,
)

from ..quantization_mappings import (
    get_static_quant_module_class,
    get_dynamic_quant_module_class,
    get_quantized_operator,
)
from ..utils import (

            

Reported by Pylint.

                  get_dynamic_quant_module_class,
    get_quantized_operator,
)
from ..utils import (
    get_swapped_custom_module_class,
    activation_is_statically_quantized,
    activation_is_int8_quantized,
    weight_is_statically_quantized,
    get_qconfig_dtypes,

            

Reported by Pylint.

                  get_qparam_dict,
)

from ..quantize import (
    is_activation_post_process,
)

from .pattern_utils import (
    register_quant_pattern,

            

Reported by Pylint.

                  is_activation_post_process,
)

from .pattern_utils import (
    register_quant_pattern,
    get_default_output_activation_post_process_map,
    Pattern,
)


            

Reported by Pylint.

                  Pattern,
)

from .utils import (
    _parent_name,
    all_node_args_have_no_tensors,
    quantize_node,
    get_per_tensor_qparams,
    get_linear_prepack_op_for_dtype,

            

Reported by Pylint.

                  get_qconv_op,
)

from ..qconfig import QConfigAny

from abc import ABC, abstractmethod
import operator
import warnings


            

Reported by Pylint.

              # tuple (activation_dtype, weight_dtype, compute_dtype)
# these are supported types for common binary ops like add/mul etc.
binary_op_all_dtypes = [
    (torch.quint8, torch.qint8, None),
    (torch.float16, torch.float16, None),
]
binary_op_float16_dtypes = [
    (torch.float16, torch.float16, None)
]

            

Reported by Pylint.

              # tuple (activation_dtype, weight_dtype, compute_dtype)
# these are supported types for common binary ops like add/mul etc.
binary_op_all_dtypes = [
    (torch.quint8, torch.qint8, None),
    (torch.float16, torch.float16, None),
]
binary_op_float16_dtypes = [
    (torch.float16, torch.float16, None)
]

            

Reported by Pylint.

              # these are supported types for common binary ops like add/mul etc.
binary_op_all_dtypes = [
    (torch.quint8, torch.qint8, None),
    (torch.float16, torch.float16, None),
]
binary_op_float16_dtypes = [
    (torch.float16, torch.float16, None)
]
binary_op_int8_dtypes = [

            

Reported by Pylint.

              # these are supported types for common binary ops like add/mul etc.
binary_op_all_dtypes = [
    (torch.quint8, torch.qint8, None),
    (torch.float16, torch.float16, None),
]
binary_op_float16_dtypes = [
    (torch.float16, torch.float16, None)
]
binary_op_int8_dtypes = [

            

Reported by Pylint.

                                  return g.op("Constant", value_s=n["value"])
                elif n.output().type().isSubtypeOf(ListType.ofInts()) or n.output().type().isSubtypeOf(ListType.ofFloats()):
                    vals = n.output().toIValue()
                    value = torch.stack([torch.tensor(v) for v in vals]) if len(vals) else []
                    return g.op("Constant", value_t=value)
                elif n.output().type().kind() == "DeviceObjType":
                    return None
                else:
                    raise RuntimeError("Unsupported prim::Constant kind: `{}`. Send a bug report.".format(

            

Reported by Pylint.

                                  return g.op("Constant", value_s=n["value"])
                elif n.output().type().isSubtypeOf(ListType.ofInts()) or n.output().type().isSubtypeOf(ListType.ofFloats()):
                    vals = n.output().toIValue()
                    value = torch.stack([torch.tensor(v) for v in vals]) if len(vals) else []
                    return g.op("Constant", value_t=value)
                elif n.output().type().kind() == "DeviceObjType":
                    return None
                else:
                    raise RuntimeError("Unsupported prim::Constant kind: `{}`. Send a bug report.".format(

            

Reported by Pylint.

                      dims = [1]
        isscalar = True
    type = type.lower()
    tensor: Union[torch.CharTensor, torch.ShortTensor,
                  torch.IntTensor, torch.LongTensor,
                  torch.HalfTensor, torch.FloatTensor,
                  torch.DoubleTensor]
    if type == "char":
        tensor = torch.CharTensor(*dims)

            

Reported by Pylint.

                      dims = [1]
        isscalar = True
    type = type.lower()
    tensor: Union[torch.CharTensor, torch.ShortTensor,
                  torch.IntTensor, torch.LongTensor,
                  torch.HalfTensor, torch.FloatTensor,
                  torch.DoubleTensor]
    if type == "char":
        tensor = torch.CharTensor(*dims)

            

Reported by Pylint.

                      isscalar = True
    type = type.lower()
    tensor: Union[torch.CharTensor, torch.ShortTensor,
                  torch.IntTensor, torch.LongTensor,
                  torch.HalfTensor, torch.FloatTensor,
                  torch.DoubleTensor]
    if type == "char":
        tensor = torch.CharTensor(*dims)
    elif type == "short":

            

Reported by Pylint.

                      isscalar = True
    type = type.lower()
    tensor: Union[torch.CharTensor, torch.ShortTensor,
                  torch.IntTensor, torch.LongTensor,
                  torch.HalfTensor, torch.FloatTensor,
                  torch.DoubleTensor]
    if type == "char":
        tensor = torch.CharTensor(*dims)
    elif type == "short":

            

Reported by Pylint.

                  type = type.lower()
    tensor: Union[torch.CharTensor, torch.ShortTensor,
                  torch.IntTensor, torch.LongTensor,
                  torch.HalfTensor, torch.FloatTensor,
                  torch.DoubleTensor]
    if type == "char":
        tensor = torch.CharTensor(*dims)
    elif type == "short":
        tensor = torch.ShortTensor(*dims)

            

Reported by Pylint.

                  type = type.lower()
    tensor: Union[torch.CharTensor, torch.ShortTensor,
                  torch.IntTensor, torch.LongTensor,
                  torch.HalfTensor, torch.FloatTensor,
                  torch.DoubleTensor]
    if type == "char":
        tensor = torch.CharTensor(*dims)
    elif type == "short":
        tensor = torch.ShortTensor(*dims)

            

Reported by Pylint.

                  tensor: Union[torch.CharTensor, torch.ShortTensor,
                  torch.IntTensor, torch.LongTensor,
                  torch.HalfTensor, torch.FloatTensor,
                  torch.DoubleTensor]
    if type == "char":
        tensor = torch.CharTensor(*dims)
    elif type == "short":
        tensor = torch.ShortTensor(*dims)
    elif type == "int":

            

Reported by Pylint.

                                torch.HalfTensor, torch.FloatTensor,
                  torch.DoubleTensor]
    if type == "char":
        tensor = torch.CharTensor(*dims)
    elif type == "short":
        tensor = torch.ShortTensor(*dims)
    elif type == "int":
        tensor = torch.IntTensor(*dims)
    elif type == "long":

            

Reported by Pylint.