import torch
from . import _functional as F
from .optimizer import Optimizer


class SparseAdam(Optimizer):
    r"""Implements lazy version of Adam algorithm suitable for sparse tensors.

    In this variant, only moments that show up in the gradient get updated, and

            

Reported by Pylint.

              import torch
from . import _functional as F
from .optimizer import Optimizer


class SparseAdam(Optimizer):
    r"""Implements lazy version of Adam algorithm suitable for sparse tensors.

    In this variant, only moments that show up in the gradient get updated, and

            

Reported by Pylint.

                                  if len(state) == 0:
                        state['step'] = 0
                        # Exponential moving average of gradient values
                        state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                        # Exponential moving average of squared gradient values
                        state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    exp_avgs.append(state['exp_avg'])
                    exp_avg_sqs.append(state['exp_avg_sq'])

            

Reported by Pylint.

                                  if len(state) == 0:
                        state['step'] = 0
                        # Exponential moving average of gradient values
                        state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                        # Exponential moving average of squared gradient values
                        state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    exp_avgs.append(state['exp_avg'])
                    exp_avg_sqs.append(state['exp_avg_sq'])

            

Reported by Pylint.

                                      # Exponential moving average of gradient values
                        state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                        # Exponential moving average of squared gradient values
                        state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    exp_avgs.append(state['exp_avg'])
                    exp_avg_sqs.append(state['exp_avg_sq'])

                    # update the steps for each param group update

            

Reported by Pylint.

                                      # Exponential moving average of gradient values
                        state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                        # Exponential moving average of squared gradient values
                        state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    exp_avgs.append(state['exp_avg'])
                    exp_avg_sqs.append(state['exp_avg_sq'])

                    # update the steps for each param group update

            

Reported by Pylint.

                          exp_avgs = []
            exp_avg_sqs = []
            state_steps = []
            eps = group['eps']
            lr = group['lr']
            beta1, beta2 = group['betas']

            for p in group['params']:
                if p.grad is not None:

            

Reported by Pylint.

                          exp_avg_sqs = []
            state_steps = []
            eps = group['eps']
            lr = group['lr']
            beta1, beta2 = group['betas']

            for p in group['params']:
                if p.grad is not None:
                    params_with_grad.append(p)

            

Reported by Pylint.

              import torch
from . import _functional as F
from .optimizer import Optimizer


class SparseAdam(Optimizer):
    r"""Implements lazy version of Adam algorithm suitable for sparse tensors.

    In this variant, only moments that show up in the gradient get updated, and

            

Reported by Pylint.

              from .optimizer import Optimizer


class SparseAdam(Optimizer):
    r"""Implements lazy version of Adam algorithm suitable for sparse tensors.

    In this variant, only moments that show up in the gradient get updated, and
    only those portions of the gradient get applied to the parameters.


            

Reported by Pylint.

              

@functional_datapipe('concat')
class ConcatIterDataPipe(IterDataPipe):
    r""" :class:`ConcatIterDataPipe`.

    Iterable DataPipe to concatenate multiple Iterable DataPipes.
    args:
        datapipes: Iterable DataPipes being concatenated

            

Reported by Pylint.

              

# This is fake class to show API, going to be replaced by the copy from torchdata
# TODO(VitalyFedyunin): Replace with valid version, documentation and tests
class IterateBuffer(IterDataPipe):
    def __init__(self, buffer):
        self.buffer = buffer

    def __iter__(self):

            

Reported by Pylint.

              
# This is fake class to show API, going to be replaced by the copy from torchdata
# TODO(VitalyFedyunin): Replace with valid version, documentation and tests
class IterateBuffer(IterDataPipe):
    def __init__(self, buffer):
        self.buffer = buffer

    def __iter__(self):
        for i in self.buffer:

            

Reported by Pylint.

              

@functional_datapipe('fork')
class ForkIterDataPipe(IterDataPipe):

    def __new__(cls, datapipe, instances):
        result = []
        buffer = list(datapipe)
        return [IterateBuffer(buffer) for i in range(instances)]

            

Reported by Pylint.

              

@functional_datapipe('fork')
class ForkIterDataPipe(IterDataPipe):

    def __new__(cls, datapipe, instances):
        result = []
        buffer = list(datapipe)
        return [IterateBuffer(buffer) for i in range(instances)]

            

Reported by Pylint.

              class ForkIterDataPipe(IterDataPipe):

    def __new__(cls, datapipe, instances):
        result = []
        buffer = list(datapipe)
        return [IterateBuffer(buffer) for i in range(instances)]


@functional_datapipe('demux')

            

Reported by Pylint.

              

@functional_datapipe('demux')
class DemultiplexerIterDataPipe(IterDataPipe):

    def __new__(cls, datapipe, instances, classifier_fn):
        result = []
        buffer = list(datapipe)


            

Reported by Pylint.

              

@functional_datapipe('demux')
class DemultiplexerIterDataPipe(IterDataPipe):

    def __new__(cls, datapipe, instances, classifier_fn):
        result = []
        buffer = list(datapipe)


            

Reported by Pylint.

              class DemultiplexerIterDataPipe(IterDataPipe):

    def __new__(cls, datapipe, instances, classifier_fn):
        result = []
        buffer = list(datapipe)

        def filter_fn(classifier_fn, i, x):
            return classifier_fn(x) == i
        return [IterateBuffer(buffer).filter(functools.partial(filter_fn, classifier_fn, i)) for i in range(instances)]

            

Reported by Pylint.

                      return [IterateBuffer(buffer).filter(functools.partial(filter_fn, classifier_fn, i)) for i in range(instances)]

@functional_datapipe('mux')
class MultiplexerIterDataPipe(IterDataPipe):

    def __init__(self, *datapipes):
        self.datapipes = datapipes

    def __iter__(self):

            

Reported by Pylint.

              import warnings

import torch
from . import comm
from torch.autograd import Function
from torch._utils import _get_device_index
from typing import List, Optional



            

Reported by Pylint.

              class Broadcast(Function):

    @staticmethod
    def forward(ctx, target_gpus, *inputs):
        assert all(i.device.type != 'cpu' for i in inputs), (
            'Broadcast function not implemented for CPU tensors'
        )
        target_gpus = [_get_device_index(x, True) for x in target_gpus]
        ctx.target_gpus = target_gpus

            

Reported by Pylint.

              class ReduceAddCoalesced(Function):

    @staticmethod
    def forward(ctx, destination, num_inputs, *grads):
        ctx.target_gpus = [grads[i].get_device() for i in range(0, len(grads), num_inputs)]

        grads_ = [grads[i:i + num_inputs]
                  for i in range(0, len(grads), num_inputs)]
        return comm.reduce_add_coalesced(grads_, destination)

            

Reported by Pylint.

              class Gather(Function):

    @staticmethod
    def forward(ctx, target_device, dim, *inputs):
        assert all(i.device.type != 'cpu' for i in inputs), (
            'Gather function not implemented for CPU tensors'
        )
        if (target_device == 'cpu'):
            ctx.target_device = 'cpu'

            

Reported by Pylint.

                      return comm.gather(inputs, ctx.dim, ctx.target_device)

    @staticmethod
    def backward(ctx, grad_output):
        scattered_grads = Scatter.apply(ctx.input_gpus, ctx.input_sizes, ctx.dim, grad_output)
        if ctx.unsqueezed_scalar:
            scattered_grads = tuple(g[0] for g in scattered_grads)
        return (None, None) + scattered_grads


            

Reported by Pylint.

              class Scatter(Function):

    @staticmethod
    def forward(ctx, target_gpus, chunk_sizes, dim, input):
        target_gpus = [_get_device_index(x, True) for x in target_gpus]
        ctx.dim = dim
        ctx.input_device = input.get_device() if input.device.type != "cpu" else -1
        streams = None
        if torch.cuda.is_available() and ctx.input_device == -1:

            

Reported by Pylint.

              class Scatter(Function):

    @staticmethod
    def forward(ctx, target_gpus, chunk_sizes, dim, input):
        target_gpus = [_get_device_index(x, True) for x in target_gpus]
        ctx.dim = dim
        ctx.input_device = input.get_device() if input.device.type != "cpu" else -1
        streams = None
        if torch.cuda.is_available() and ctx.input_device == -1:

            

Reported by Pylint.

              
def _get_stream(device: int):
    """Gets a background stream for copying between CPU and GPU"""
    global _streams
    if device == -1:
        return None
    if _streams is None:
        _streams = [None] * torch.cuda.device_count()
    if _streams[device] is None:

            

Reported by Pylint.

              import warnings

import torch
from . import comm
from torch.autograd import Function
from torch._utils import _get_device_index
from typing import List, Optional



            

Reported by Pylint.

              
import torch
from . import comm
from torch.autograd import Function
from torch._utils import _get_device_index
from typing import List, Optional


class Broadcast(Function):

            

Reported by Pylint.

              from .module import Module
from .. import functional as F

from torch import Tensor
from typing import Optional
from ..common_types import _size_2_t, _ratio_2_t, _size_any_t, _ratio_any_t


class Upsample(Module):

            

Reported by Pylint.

              from .module import Module
from .. import functional as F

from torch import Tensor
from typing import Optional
from ..common_types import _size_2_t, _ratio_2_t, _size_any_t, _ratio_any_t


class Upsample(Module):

            

Reported by Pylint.

              
from torch import Tensor
from typing import Optional
from ..common_types import _size_2_t, _ratio_2_t, _size_any_t, _ratio_any_t


class Upsample(Module):
    r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.


            

Reported by Pylint.

                      self.mode = mode
        self.align_corners = align_corners

    def forward(self, input: Tensor) -> Tensor:
        return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners)

    def extra_repr(self) -> str:
        if self.scale_factor is not None:
            info = 'scale_factor=' + str(self.scale_factor)

            

Reported by Pylint.

              from .module import Module
from .. import functional as F

from torch import Tensor
from typing import Optional
from ..common_types import _size_2_t, _ratio_2_t, _size_any_t, _ratio_any_t


class Upsample(Module):

            

Reported by Pylint.

              from .module import Module
from .. import functional as F

from torch import Tensor
from typing import Optional
from ..common_types import _size_2_t, _ratio_2_t, _size_any_t, _ratio_any_t


class Upsample(Module):

            

Reported by Pylint.

              from .. import functional as F

from torch import Tensor
from typing import Optional
from ..common_types import _size_2_t, _ratio_2_t, _size_any_t, _ratio_any_t


class Upsample(Module):
    r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.

            

Reported by Pylint.

              
    The input data is assumed to be of the form
    `minibatch x channels x [optional depth] x [optional height] x width`.
    Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor.

    The algorithms available for upsampling are nearest neighbor and linear,
    bilinear, bicubic and trilinear for 3D, 4D and 5D input Tensor,
    respectively.


            

Reported by Pylint.

                  Args:
        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int], optional):
            output spatial sizes
        scale_factor (float or Tuple[float] or Tuple[float, float] or Tuple[float, float, float], optional):
            multiplier for spatial size. Has to match input size if it is a tuple.
        mode (str, optional): the upsampling algorithm: one of ``'nearest'``,
            ``'linear'``, ``'bilinear'``, ``'bicubic'`` and ``'trilinear'``.
            Default: ``'nearest'``
        align_corners (bool, optional): if ``True``, the corner pixels of the input

            

Reported by Pylint.

                          ``'linear'``, ``'bilinear'``, or ``'trilinear'``. Default: ``False``

    Shape:
        - Input: :math:`(N, C, W_{in})`, :math:`(N, C, H_{in}, W_{in})` or :math:`(N, C, D_{in}, H_{in}, W_{in})`
        - Output: :math:`(N, C, W_{out})`, :math:`(N, C, H_{out}, W_{out})`
          or :math:`(N, C, D_{out}, H_{out}, W_{out})`, where

    .. math::
        D_{out} = \left\lfloor D_{in} \times \text{scale\_factor} \right\rfloor

            

Reported by Pylint.

              
from torch.fx.graph import Graph

from ..utils import (
    get_combined_dict
)

from .pattern_utils import (
    get_default_fusion_patterns,

            

Reported by Pylint.

                  get_combined_dict
)

from .pattern_utils import (
    get_default_fusion_patterns,
)

from .match_utils import is_match


            

Reported by Pylint.

                  get_default_fusion_patterns,
)

from .match_utils import is_match

from .graph_module import (
    FusedGraphModule
)


            

Reported by Pylint.

              
from .match_utils import is_match

from .graph_module import (
    FusedGraphModule
)

from .fusion_patterns import *  # noqa: F401,F403


            

Reported by Pylint.

                  FusedGraphModule
)

from .fusion_patterns import *  # noqa: F401,F403

from .quantization_types import Pattern

from typing import Callable, Tuple


            

Reported by Pylint.

              
from .fusion_patterns import *  # noqa: F401,F403

from .quantization_types import Pattern

from typing import Callable, Tuple


class Fuser:

            

Reported by Pylint.

                  def _find_matches(
            self, root: GraphModule, graph: Graph,
            patterns: Dict[Pattern, Callable]
    ) -> Dict[str, Tuple[Node, FuseHandler]]:
        modules = dict(root.named_modules())
        match_map : Dict[str, Tuple[Node, FuseHandler]] = {}  # node name -> (root_node, match_value)

        def apply_match(pattern, node, match):
            if isinstance(pattern, tuple):

            

Reported by Pylint.

                          patterns: Dict[Pattern, Callable]
    ) -> Dict[str, Tuple[Node, FuseHandler]]:
        modules = dict(root.named_modules())
        match_map : Dict[str, Tuple[Node, FuseHandler]] = {}  # node name -> (root_node, match_value)

        def apply_match(pattern, node, match):
            if isinstance(pattern, tuple):
                s, *args = pattern
                apply_match(s, node, match)

            

Reported by Pylint.

                  FusedGraphModule
)

from .fusion_patterns import *  # noqa: F401,F403

from .quantization_types import Pattern

from typing import Callable, Tuple


            

Reported by Pylint.

              
        input_root = model
        input_graph = model.graph
        self.modules = dict(input_root.named_modules())

        additional_fusion_patterns = \
            fuse_custom_config_dict.get("additional_fusion_pattern", {})
        fusion_patterns = get_combined_dict(
            get_default_fusion_patterns(), additional_fusion_patterns)

            

Reported by Pylint.

              import torch
from . import _functional as F
from ..optimizer import Optimizer
from collections import defaultdict

class ASGD(Optimizer):
    """Implements Averaged Stochastic Gradient Descent.

    It has been proposed in `Acceleration of stochastic approximation by

            

Reported by Pylint.

              import torch
from . import _functional as F
from ..optimizer import Optimizer
from collections import defaultdict

class ASGD(Optimizer):
    """Implements Averaged Stochastic Gradient Descent.

    It has been proposed in `Acceleration of stochastic approximation by

            

Reported by Pylint.

                                      state['step'] = 0
                        state['eta'] = group['lr']
                        state['mu'] = 1
                        state['ax'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    state['step'] += 1
                    states.append(state)

            F.asgd(params_with_grad,

            

Reported by Pylint.

                                      state['step'] = 0
                        state['eta'] = group['lr']
                        state['mu'] = 1
                        state['ax'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    state['step'] += 1
                    states.append(state)

            F.asgd(params_with_grad,

            

Reported by Pylint.

              
            for _, per_dtype_grads in per_device_and_dtype_grads.items():
                for grads in per_dtype_grads.values():
                    torch._foreach_zero_(grads)

            

Reported by Pylint.

              
        return loss

    # TODO: refactor to a base class once foreach ops are in a good shape.
    def zero_grad(self, set_to_none: bool = False):
        per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))
        for group in self.param_groups:
            for p in group['params']:
                if p.grad is not None:

            

Reported by Pylint.

              
            for _, per_dtype_grads in per_device_and_dtype_grads.items():
                for grads in per_dtype_grads.values():
                    torch._foreach_zero_(grads)

            

Reported by Pylint.

              import torch
from . import _functional as F
from ..optimizer import Optimizer
from collections import defaultdict

class ASGD(Optimizer):
    """Implements Averaged Stochastic Gradient Descent.

    It has been proposed in `Acceleration of stochastic approximation by

            

Reported by Pylint.

              import torch
from . import _functional as F
from ..optimizer import Optimizer
from collections import defaultdict

class ASGD(Optimizer):
    """Implements Averaged Stochastic Gradient Descent.

    It has been proposed in `Acceleration of stochastic approximation by

            

Reported by Pylint.

                      https://dl.acm.org/citation.cfm?id=131098
    """

    def __init__(self, params, lr=1e-2, lambd=1e-4, alpha=0.75, t0=1e6, weight_decay=0):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= weight_decay:
            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))


            

Reported by Pylint.

              import torch
from . import _functional as F
from .optimizer import Optimizer


class RAdam(Optimizer):
    r"""implements RAdam algorithm.

    It has been proposed in `On the variance of the adaptive learning rate and beyond`_.

            

Reported by Pylint.

              import torch
from . import _functional as F
from .optimizer import Optimizer


class RAdam(Optimizer):
    r"""implements RAdam algorithm.

    It has been proposed in `On the variance of the adaptive learning rate and beyond`_.

            

Reported by Pylint.

                                  if len(state) == 0:
                        state['step'] = 0
                        # Exponential moving average of gradient values
                        state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                        # Exponential moving average of squared gradient values
                        state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    exp_avgs.append(state['exp_avg'])
                    exp_avg_sqs.append(state['exp_avg_sq'])

            

Reported by Pylint.

                                  if len(state) == 0:
                        state['step'] = 0
                        # Exponential moving average of gradient values
                        state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                        # Exponential moving average of squared gradient values
                        state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    exp_avgs.append(state['exp_avg'])
                    exp_avg_sqs.append(state['exp_avg_sq'])

            

Reported by Pylint.

                                      # Exponential moving average of gradient values
                        state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                        # Exponential moving average of squared gradient values
                        state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    exp_avgs.append(state['exp_avg'])
                    exp_avg_sqs.append(state['exp_avg_sq'])

                    # update the steps for each param group update

            

Reported by Pylint.

                                      # Exponential moving average of gradient values
                        state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                        # Exponential moving average of squared gradient values
                        state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    exp_avgs.append(state['exp_avg'])
                    exp_avg_sqs.append(state['exp_avg_sq'])

                    # update the steps for each param group update

            

Reported by Pylint.

                          grads = []
            exp_avgs = []
            exp_avg_sqs = []
            max_exp_avg_sqs = []
            state_steps = []
            beta1, beta2 = group['betas']

            for p in group['params']:
                if p.grad is not None:

            

Reported by Pylint.

              import torch
from . import _functional as F
from .optimizer import Optimizer


class RAdam(Optimizer):
    r"""implements RAdam algorithm.

    It has been proposed in `On the variance of the adaptive learning rate and beyond`_.

            

Reported by Pylint.

              from .optimizer import Optimizer


class RAdam(Optimizer):
    r"""implements RAdam algorithm.

    It has been proposed in `On the variance of the adaptive learning rate and beyond`_.

    Args:

            

Reported by Pylint.

                      https://arxiv.org/abs/1908.03265
    """

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=0):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))

            

Reported by Pylint.

                              assert not isinstance(target, str)
                dispatched = boolean_dispatched[target]
                if_true, if_false = dispatched['if_true'], dispatched['if_false']
                # TODO: can we emit the union of these? What are the implications on TorchScript
                # compilation?
                if inspect.signature(if_true).return_annotation != inspect.signature(if_false).return_annotation:
                    return super().call_function(target, args, kwargs)
                target_for_analysis = if_true


            

Reported by Pylint.

                                  raise RuntimeError(f'Node referenced nonextent target {".".join(atoms[:i])}!')
                module_itr = getattr(module_itr, atom)

            maybe_inferred_ts_type = torch._C._jit_try_infer_type(module_itr)
            if maybe_inferred_ts_type.success():
                python_type = _torchscript_type_to_python_type(maybe_inferred_ts_type.type())
                attr_proxy.node.type = python_type if not attr_proxy.node.type else attr_proxy.node.type

        return attr_proxy

            

Reported by Pylint.

                                  raise RuntimeError(f'Node referenced nonextent target {".".join(atoms[:i])}!')
                module_itr = getattr(module_itr, atom)

            maybe_inferred_ts_type = torch._C._jit_try_infer_type(module_itr)
            if maybe_inferred_ts_type.success():
                python_type = _torchscript_type_to_python_type(maybe_inferred_ts_type.type())
                attr_proxy.node.type = python_type if not attr_proxy.node.type else attr_proxy.node.type

        return attr_proxy

            

Reported by Pylint.

              import torch
import torch.fx
import inspect
from typing import Any, Dict, Optional, Tuple
from torch.fx.node import Argument, Target
from torch._jit_internal import boolean_dispatched
from torch.fx.operator_schemas import _torchscript_type_to_python_type

from torch.fx import Transformer

            

Reported by Pylint.

              import torch
import torch.fx
import inspect
from typing import Any, Dict, Optional, Tuple
from torch.fx.node import Argument, Target
from torch._jit_internal import boolean_dispatched
from torch.fx.operator_schemas import _torchscript_type_to_python_type

from torch.fx import Transformer

            

Reported by Pylint.

              import torch
import torch.fx
import inspect
from typing import Any, Dict, Optional, Tuple
from torch.fx.node import Argument, Target
from torch._jit_internal import boolean_dispatched
from torch.fx.operator_schemas import _torchscript_type_to_python_type

from torch.fx import Transformer

            

Reported by Pylint.

                          target_for_analysis = target
            if target in boolean_dispatched:
                # HACK: `boolean_dispatch` as used in `torch.nn.functional` makes it so that we have
                # a 2-way dispatch based on a boolean value. Here we check that the `true` and `false`
                # branches of the dispatch have exactly the same signature. If they do, use the `true`
                # branch signature for analysis. Otherwise, leave this un-normalized
                assert not isinstance(target, str)
                dispatched = boolean_dispatched[target]
                if_true, if_false = dispatched['if_true'], dispatched['if_false']

            

Reported by Pylint.

                          if target in boolean_dispatched:
                # HACK: `boolean_dispatch` as used in `torch.nn.functional` makes it so that we have
                # a 2-way dispatch based on a boolean value. Here we check that the `true` and `false`
                # branches of the dispatch have exactly the same signature. If they do, use the `true`
                # branch signature for analysis. Otherwise, leave this un-normalized
                assert not isinstance(target, str)
                dispatched = boolean_dispatched[target]
                if_true, if_false = dispatched['if_true'], dispatched['if_false']
                # TODO: can we emit the union of these? What are the implications on TorchScript

            

Reported by Pylint.

                              # a 2-way dispatch based on a boolean value. Here we check that the `true` and `false`
                # branches of the dispatch have exactly the same signature. If they do, use the `true`
                # branch signature for analysis. Otherwise, leave this un-normalized
                assert not isinstance(target, str)
                dispatched = boolean_dispatched[target]
                if_true, if_false = dispatched['if_true'], dispatched['if_false']
                # TODO: can we emit the union of these? What are the implications on TorchScript
                # compilation?
                if inspect.signature(if_true).return_annotation != inspect.signature(if_false).return_annotation:

            

Reported by Bandit.

                              if_true, if_false = dispatched['if_true'], dispatched['if_false']
                # TODO: can we emit the union of these? What are the implications on TorchScript
                # compilation?
                if inspect.signature(if_true).return_annotation != inspect.signature(if_false).return_annotation:
                    return super().call_function(target, args, kwargs)
                target_for_analysis = if_true

            python_ret_type = self._extract_python_return_type(target_for_analysis)


            

Reported by Pylint.

              import math
import torch
from . import _functional as F
from .optimizer import Optimizer


class ASGD(Optimizer):
    """Implements Averaged Stochastic Gradient Descent.


            

Reported by Pylint.

              import math
import torch
from . import _functional as F
from .optimizer import Optimizer


class ASGD(Optimizer):
    """Implements Averaged Stochastic Gradient Descent.


            

Reported by Pylint.

                                      state['step'] = 0
                        state['eta'] = group['lr']
                        state['mu'] = 1
                        state['ax'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    mus.append(state['mu'])
                    axs.append(state['ax'])
                    etas.append(state['eta'])


            

Reported by Pylint.

                                      state['step'] = 0
                        state['eta'] = group['lr']
                        state['mu'] = 1
                        state['ax'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                    mus.append(state['mu'])
                    axs.append(state['ax'])
                    etas.append(state['eta'])


            

Reported by Pylint.

                                 lambd=group['lambd'])

            # update eta and mu
            for p, mu, eta in zip(params_with_grad, mus, etas):
                state = self.state[p]
                state['eta'] = (group['lr'] /
                                math.pow((1 + group['lambd'] * group['lr'] * state['step']), group['alpha']))
                state['mu'] = 1 / max(1, state['step'] - group['t0'])


            

Reported by Pylint.

                                 lambd=group['lambd'])

            # update eta and mu
            for p, mu, eta in zip(params_with_grad, mus, etas):
                state = self.state[p]
                state['eta'] = (group['lr'] /
                                math.pow((1 + group['lambd'] * group['lr'] * state['step']), group['alpha']))
                state['mu'] = 1 / max(1, state['step'] - group['t0'])


            

Reported by Pylint.

              import math
import torch
from . import _functional as F
from .optimizer import Optimizer


class ASGD(Optimizer):
    """Implements Averaged Stochastic Gradient Descent.


            

Reported by Pylint.

              from .optimizer import Optimizer


class ASGD(Optimizer):
    """Implements Averaged Stochastic Gradient Descent.

    It has been proposed in `Acceleration of stochastic approximation by
    averaging`_.


            

Reported by Pylint.

                      https://dl.acm.org/citation.cfm?id=131098
    """

    def __init__(self, params, lr=1e-2, lambd=1e-4, alpha=0.75, t0=1e6, weight_decay=0):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= weight_decay:
            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))


            

Reported by Pylint.

                      https://dl.acm.org/citation.cfm?id=131098
    """

    def __init__(self, params, lr=1e-2, lambd=1e-4, alpha=0.75, t0=1e6, weight_decay=0):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= weight_decay:
            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))


            

Reported by Pylint.

              import torch

from . import _functional as F
from .optimizer import Optimizer


class Adadelta(Optimizer):
    """Implements Adadelta algorithm.


            

Reported by Pylint.

              import torch

from . import _functional as F
from .optimizer import Optimizer


class Adadelta(Optimizer):
    """Implements Adadelta algorithm.


            

Reported by Pylint.

                              # Lazy state initialization
                if len(state) == 0:
                    state['step'] = 0
                    state['square_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                    state['acc_delta'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                square_avgs.append(state['square_avg'])
                acc_deltas.append(state['acc_delta'])


            

Reported by Pylint.

                              # Lazy state initialization
                if len(state) == 0:
                    state['step'] = 0
                    state['square_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                    state['acc_delta'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                square_avgs.append(state['square_avg'])
                acc_deltas.append(state['acc_delta'])


            

Reported by Pylint.

                              if len(state) == 0:
                    state['step'] = 0
                    state['square_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                    state['acc_delta'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                square_avgs.append(state['square_avg'])
                acc_deltas.append(state['acc_delta'])

                state['step'] += 1

            

Reported by Pylint.

                              if len(state) == 0:
                    state['step'] = 0
                    state['square_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
                    state['acc_delta'] = torch.zeros_like(p, memory_format=torch.preserve_format)

                square_avgs.append(state['square_avg'])
                acc_deltas.append(state['acc_delta'])

                state['step'] += 1

            

Reported by Pylint.

              import torch

from . import _functional as F
from .optimizer import Optimizer


class Adadelta(Optimizer):
    """Implements Adadelta algorithm.


            

Reported by Pylint.

              from .optimizer import Optimizer


class Adadelta(Optimizer):
    """Implements Adadelta algorithm.

    It has been proposed in `ADADELTA: An Adaptive Learning Rate Method`__.

    Args:

            

Reported by Pylint.

                  __ https://arxiv.org/abs/1212.5701
    """

    def __init__(self, params, lr=1.0, rho=0.9, eps=1e-6, weight_decay=0):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= rho <= 1.0:
            raise ValueError("Invalid rho value: {}".format(rho))
        if not 0.0 <= eps:

            

Reported by Pylint.

                  __ https://arxiv.org/abs/1212.5701
    """

    def __init__(self, params, lr=1.0, rho=0.9, eps=1e-6, weight_decay=0):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= rho <= 1.0:
            raise ValueError("Invalid rho value: {}".format(rho))
        if not 0.0 <= eps:

            

Reported by Pylint.