For more, see go directive in the Go Modules Reference.

go minimum-go-version
For more, see module directive in the Go Modules Reference.

module module-path
For more, see require directive in the Go Modules Reference.

require module-path module-version
For more, see replace directive in the Go Modules Reference.

replace module-path [module-version] => replacement-path [replacement-version]
For more, see exclude directive in the Go Modules Reference.

exclude module-path module-version
For more, see retract directive in the Go Modules Reference.

retract version // rationale
retract [version-low,version-high] // rationale

edit your apache config file

# This is the main Apache server configuration file.  It contains the
# configuration directives that give the server its instructions.
# See for detailed information about
# the directives and /usr/share/doc/apache2/README.Debian about Debian specific
# hints.
# Summary of how the Apache 2 configuration works in Debian:
# The Apache 2 web server configuration in Debian is quite different to
# upstream's suggested way to configure the web server. This is because Debian's
# default Apache2 installation attempts to make adding and removing modules,
# virtual hosts, and extra configuration directives as flexible as possible, in
# order to make automating the changes and administering the server as easy as
# possible.

# It is split into several files forming the configuration hierarchy outlined
# below, all located in the /etc/apache2/ directory:
#   /etc/apache2/
#   |-- apache2.conf
#   |   `--  ports.conf
#   |-- mods-enabled
#   |   |-- *.load
#   |   `-- *.conf
#   |-- conf-enabled
#   |   `-- *.conf
#   `-- sites-enabled
#       `-- *.conf
# * apache2.conf is the main configuration file (this file). It puts the pieces
#   together by including all remaining configuration files when starting up the
#   web server.
# * ports.conf is always included from the main configuration file. It is
#   supposed to determine listening ports for incoming connections which can be
#   customized anytime.
# * Configuration files in the mods-enabled/, conf-enabled/ and sites-enabled/
#   directories contain particular configuration snippets which manage modules,
#   global configuration fragments, or virtual host configurations,
#   respectively.
#   They are activated by symlinking available configuration files from their
#   respective *-available/ counterparts. These should be managed by using our
#   helpers a2enmod/a2dismod, a2ensite/a2dissite and a2enconf/a2disconf. See
#   their respective man pages for detailed information.
# * The binary is called apache2. Due to the use of environment variables, in
#   the default configuration, apache2 needs to be started/stopped with
#   /etc/init.d/apache2 or apache2ctl. Calling /usr/bin/apache2 directly will not
#   work with the default configuration.

# Global configuration

# ServerRoot: The top of the directory tree under which the server's
# configuration, error, and log files are kept.
# NOTE!  If you intend to place this on an NFS (or otherwise network)
# mounted filesystem then please read the Mutex documentation (available
# at <URL:>);
# you will save yourself a lot of trouble.
# Do NOT add a slash at the end of the directory path.
#ServerRoot "/etc/apache2"

# The accept serialization lock file MUST BE STORED ON A LOCAL DISK.
Mutex file:${APACHE_LOCK_DIR} default

# PidFile: The file in which the server should record its process
# identification number when it starts.
# This needs to be set in /etc/apache2/envvars

# Timeout: The number of seconds before receives and sends time out.
Timeout 300

# KeepAlive: Whether or not to allow persistent connections (more than
# one request per connection). Set to "Off" to deactivate.
KeepAlive On

# MaxKeepAliveRequests: The maximum number of requests to allow
# during a persistent connection. Set to 0 to allow an unlimited amount.
# We recommend you leave this number high, for maximum performance.
MaxKeepAliveRequests 100

# KeepAliveTimeout: Number of seconds to wait for the next request from the
# same client on the same connection.
KeepAliveTimeout 5

# These need to be set in /etc/apache2/envvars

# HostnameLookups: Log the names of clients or just their IP addresses
# e.g., (on) or (off).
# The default is off because it'd be overall better for the net if people
# had to knowingly turn this feature on, since enabling it means that
# each client request will result in AT LEAST one lookup request to the
# nameserver.
HostnameLookups Off

# ErrorLog: The location of the error log file.
# If you do not specify an ErrorLog directive within a <VirtualHost>
# container, error messages relating to that virtual host will be
# logged here.  If you *do* define an error logfile for a <VirtualHost>
# container, that host's errors will be logged there and not here.
ErrorLog /var/www/html/error.log

# LogLevel: Control the severity of messages logged to the error_log.
# Available values: trace8, ..., trace1, debug, info, notice, warn,
# error, crit, alert, emerg.
# It is also possible to configure the log level for particular modules, e.g.
# "LogLevel info ssl:warn"
LogLevel warn

# Include module configuration:
IncludeOptional mods-enabled/*.load
IncludeOptional mods-enabled/*.conf

# Include list of ports to listen on
Include ports.conf

# Sets the default security model of the Apache2 HTTPD server. It does
# not allow access to the root filesystem outside of /usr/share and /var/www.
# The former is used by web applications packaged in Debian,
# the latter may be used for local directories served by the web server. If
# your system is serving content from a sub-directory in /srv you must allow
# access here, or in any related virtual host.
<Directory />
    Options FollowSymLinks
    AllowOverride All
   # Require all denied

<Directory /usr/share>
    AllowOverride None
    Require all granted

<Directory /var/www/>
    Options Indexes FollowSymLinks
    AllowOverride None
    Require all granted

#<Directory /srv/>
#   Options Indexes FollowSymLinks
#   AllowOverride None
#   Require all granted
<Directory "/var/www/html">
Allowoverride All

# AccessFileName: The name of the file to look for in each directory
# for additional configuration directives.  See also the AllowOverride
# directive.
AccessFileName .htaccess

# The following lines prevent .htaccess and .htpasswd files from being
# viewed by Web clients.
<FilesMatch "^\.ht">
    Require all denied

# The following directives define some format nicknames for use with
# a CustomLog directive.
# These deviate from the Common Log Format definitions in that they use %O
# (the actual bytes sent including headers) instead of %b (the size of the
# requested file), because the latter makes it impossible to detect partial
# requests.
# Note that the use of %{X-Forwarded-For}i instead of %h is not recommended.
# Use mod_remoteip instead.
LogFormat "%v:%p %h %l %u %t \"%r\" %>s %O \"%{Referer}i\" \"%{User-Agent}i\"" vhost_combined
LogFormat "%h %l %u %t \"%r\" %>s %O \"%{Referer}i\" \"%{User-Agent}i\"" combined
LogFormat "%h %l %u %t \"%r\" %>s %O" common
LogFormat "%{Referer}i -> %U" referer
LogFormat "%{User-agent}i" agent

# Include of directories ignores editors' and dpkg's backup files,
# see README.Debian for details.

# Include generic snippets of statements
IncludeOptional conf-enabled/*.conf

# Include the virtual host configurations:
IncludeOptional sites-enabled/*.conf

# vim: syntax=apache ts=4 sw=4 sts=4 sr noet
Include /etc/phpmyadmin/apache.conf
<VirtualHost *.80>
    DocumentRoot "/var/www/html/"
LoadModule rewrite_module modules/
<IfModule mod_suphp.c>
suPHP_UserGroup root root

goto config.php file and just add ob_start(); method it will work...please try this..

go to application/config/config.php anf my config file is --

<?php  if ( ! defined('BASEPATH')) exit('No direct script access allowed');



| Base Site URL



| URL to your CodeIgniter root. Typically this will be your base URL,

| WITH a trailing slash:




| If this is not set then CodeIgniter will guess the protocol, domain and

| path to your installation.



    $config['base_url'] = 'http://localhost/smspanel/';



| Index File



| Typically this will be your index.php file, unless you've renamed it to

    | something else. If you are using mod_rewrite to remove the page set this

| variable so that it is blank.



$config['index_page'] = '';






| This item determines which server global should be used to retrieve the

| URI string.  The default setting of 'AUTO' works for most servers.

| If your links do not seem to work, try one of the other delicious flavors:


| 'AUTO'            Default - auto detects

| 'PATH_INFO'       Uses the PATH_INFO






$config['uri_protocol'] = 'AUTO';



| URL suffix



| This option allows you to add a suffix to all URLs generated by CodeIgniter.

| For more information please see the user guide:




$config['url_suffix'] = '';



| Default Language



| This determines which set of language files should be used. Make sure

| there is an available translation if you intend to use something other

| than english.



$config['language'] = 'english';



| Default Character Set



| This determines which character set is used by default in various methods

| that require a character set to be provided.



$config['charset'] = 'UTF-8';



| Enable/Disable System Hooks



| If you would like to use the 'hooks' feature you must enable it by

| setting this variable to TRUE (boolean).  See the user guide for details.



$config['enable_hooks'] = false;



| Class Extension Prefix



| This item allows you to set the filename/classname prefix when extending

| native libraries.  For more information please see the user guide:






$config['subclass_prefix'] = 'MY_';



| Allowed URL Characters



| This lets you specify with a regular expression which characters are permitted

| within your URLs.  When someone tries to submit a URL with disallowed

| characters they will get a warning message.


| As a security measure you are STRONGLY encouraged to restrict URLs to

| as few characters as possible.  By default only these are allowed: a-z 0-9~%.:_-


| Leave blank to allow all characters -- but only if you are insane.





$config['permitted_uri_chars'] = 'a-z 0-9~%.:_\-';



| Enable Query Strings



| By default CodeIgniter uses search-engine friendly segment based URLs:



| By default CodeIgniter enables access to the $_GET array.  If for some

| reason you would like to disable it, set 'allow_get_array' to FALSE.


| You can optionally enable standard query string based URLs:



| Options are: TRUE or FALSE (boolean)


| The other items let you set the query string 'words' that will

| invoke your controllers and its functions:



| Please note that some of the helpers won't work as expected when

| this feature is enabled, since CodeIgniter is designed primarily to

| use segment based URLs.



$config['allow_get_array']      = TRUE;

$config['enable_query_strings'] = FALSE;

$config['controller_trigger']   = 'c';

$config['function_trigger'] = 'm';

$config['directory_trigger']    = 'd'; // experimental not currently in use



| Error Logging Threshold



| If you have enabled error logging, you can set an error threshold to

| determine what gets logged. Threshold options are:

| You can enable error logging by setting a threshold over zero. The

| threshold determines what gets logged. Threshold options are:


|   0 = Disables logging, Error logging TURNED OFF

|   1 = Error Messages (including PHP errors)

|   2 = Debug Messages

|   3 = Informational Messages

|   4 = All Messages


| For a live site you'll usually only enable Errors (1) to be logged otherwise

| your log files will fill up very fast.



$config['log_threshold'] = 1;



| Error Logging Directory Path



| Leave this BLANK unless you would like to set something other than the default

| application/logs/ folder. Use a full server path with trailing slash.



$config['log_path'] = '';



| Date Format for Logs



| Each item that is logged has an associated date. You can use PHP date

| codes to set your own date formatting



$config['log_date_format'] = 'Y-m-d H:i:s';



| Cache Directory Path



| Leave this BLANK unless you would like to set something other than the default

| system/cache/ folder.  Use a full server path with trailing slash.



$config['cache_path'] = '';



| Encryption Key



| If you use the Encryption class or the Session class you

| MUST set an encryption key.  See the user guide for info.



$config['encryption_key'] = '100F72504334360FDEEB3A699E';



| Session Variables



| 'sess_cookie_name'        = the name you want for the cookie

| 'sess_expiration'         = the number of SECONDS you want the session to last.

|   by default sessions last 7200 seconds (two hours).  Set to zero for no expiration.

| 'sess_expire_on_close'    = Whether to cause the session to expire automatically

|   when the browser window is closed

| 'sess_encrypt_cookie'     = Whether to encrypt the cookie

| 'sess_use_database'       = Whether to save the session data to a database

| 'sess_table_name'         = The name of the session database table

| 'sess_match_ip'           = Whether to match the user's IP address when reading the session data

| 'sess_match_useragent'    = Whether to match the User Agent when reading the session data

| 'sess_time_to_update'     = how many seconds between CI refreshing Session Information



$config['sess_cookie_name']     = 'ci_session';

$config['sess_expiration']      = 7200;

$config['sess_expire_on_close'] = FALSE;

$config['sess_encrypt_cookie']  = FALSE;

$config['sess_use_database']    = FALSE;

$config['sess_table_name']      = 'ci_sessions';

$config['sess_match_ip']        = FALSE;

$config['sess_match_useragent'] = TRUE;

$config['sess_time_to_update']  = 300;



| Cookie Related Variables



| 'cookie_prefix' = Set a prefix if you need to avoid collisions

| 'cookie_domain' = Set to for site-wide cookies

| 'cookie_path'   =  Typically will be a forward slash

| 'cookie_secure' =  Cookies will only be set if a secure HTTPS connection exists.



$config['cookie_prefix']    = "";

$config['cookie_domain']    = "";

$config['cookie_path']      = "/";

$config['cookie_secure']    = FALSE;



| Global XSS Filtering



| Determines whether the XSS filter is always active when GET, POST or

| COOKIE data is encountered



$config['global_xss_filtering'] = FALSE;



| Cross Site Request Forgery


| Enables a CSRF cookie token to be set. When set to TRUE, token will be

| checked on a submitted form. If you are accepting user data, it is strongly

| recommended CSRF protection be enabled.


| 'csrf_token_name' = The token name

| 'csrf_cookie_name' = The cookie name

| 'csrf_expire' = The number in seconds the token should expire.


$config['csrf_protection'] = FALSE;

$config['csrf_token_name'] = 'csrf_test_name';

$config['csrf_cookie_name'] = 'csrf_cookie_name';

$config['csrf_expire'] = 7200;



| Output Compression



| Enables Gzip output compression for faster page loads.  When enabled,

| the output class will test whether your server supports Gzip.

| Even if it does, however, not all browsers support compression

| so enable only if you are reasonably sure your visitors can handle it.


| VERY IMPORTANT:  If you are getting a blank page when compression is enabled it

| means you are prematurely outputting something to your browser. It could

| even be a line of whitespace at the end of one of your scripts.  For

| compression to work, nothing can be sent before the output buffer is called

| by the output class.  Do not 'echo' any values with compression enabled.



$config['compress_output'] = FALSE;



| Master Time Reference



| Options are 'local' or 'gmt'.  This pref tells the system whether to use

| your server's local time as the master 'now' reference, or convert it to

| GMT.  See the 'date helper' page of the user guide for information

| regarding date handling.



$config['time_reference'] = 'local';



| Rewrite PHP Short Tags



| If your PHP installation does not have short tag support enabled CI

| can rewrite the tags on-the-fly, enabling you to utilize that syntax

| in your view files.  Options are TRUE or FALSE (boolean)



$config['rewrite_short_tags'] = FALSE;



| Reverse Proxy IPs



| If your server is behind a reverse proxy, you must whitelist the proxy IP

| addresses from which CodeIgniter should trust the HTTP_X_FORWARDED_FOR

| header in order to properly identify the visitor's IP address.

| Comma-delimited, e.g. ','



$config['proxy_ips'] = '';
/* End of file config.php */

/* Location: ./application/config/config.php */


This module contains functionality to support the JIT's scripting frontend, notably:
    - torch.jit.script

This is not intended to be imported directly; please use the exposed
functionalities in `torch.jit`.
import functools
import collections
import enum
import inspect
import copy
import pickle
import warnings
from typing import Any, Dict, List, Tuple, Union, Callable

import torch
import torch._jit_internal as _jit_internal
from torch.utils import set_module
from torch.jit._recursive import ScriptMethodStub, wrap_cpp_module, infer_methods_to_compile, _compile_and_register_class
from torch.nn import Module
from torch.jit._state import _enabled
from torch.jit._builtins import _register_builtin
from torch._six import with_metaclass
from torch.jit.frontend import get_jit_def, get_default_args, get_jit_class_def
from torch._jit_internal import _qualified_name
from torch.jit._fuser import _graph_for
from torch.jit._state import (
from torch.overrides import (
    has_torch_function, has_torch_function_unary, has_torch_function_variadic)
from torch.package import PackageExporter, PackageImporter
from ._serialization import validate_map_location

from torch.jit._monkeytype_config import (
    JitTypeTraceConfig ,
from torch._classes import classes

type_trace_db = JitTypeTraceStore()  # DB to hold all call traces from MonkeyType

torch._C.ScriptMethod.graph_for = _graph_for  # type: ignore[attr-defined]
torch._C.ScriptFunction.graph_for = _graph_for  # type: ignore[attr-defined]
ScriptFunction = torch._C.ScriptFunction
ScriptFunction.__doc__ = """
Functionally equivalent to a :class:`ScriptModule`, but represents a single
function and does not have any attributes or Parameters.
set_module(ScriptFunction, "torch.jit")

if _enabled:
    Attribute = collections.namedtuple("Attribute", ["value", "type"])

[docs]    def Attribute(value, type):  # type: ignore[no-redef]
        return value

Attribute.__doc__ = """
    This method is a pass-through function that returns `value`, mostly
    used to indicate to the TorchScript compiler that the left-hand side
    expression is a class instance attribute with type of `type`. Note that
    `torch.jit.Attribute` should only be used in `__init__` method of `nn.Module`

    Though TorchScript can infer correct type for most Python expressions, there are some cases where
    type inference can be wrong, including:

    - Empty containers like `[]` and `{}`, which TorchScript assumes to be container of `Tensor`
    - Optional types like `Optional[T]` but assigned a valid value of type `T`, TorchScript would assume
      it is type `T` rather than `Optional[T]`

    In eager mode, it is simply a pass-through function that returns `value`
    without other implications.


    .. testcode::

        import torch
        from typing import Dict

        class AttributeModule(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
       = torch.jit.Attribute(0.1, float)

                # we should be able to use as a float here
                assert 0.0 <

                self.names_ages = torch.jit.Attribute({}, Dict[str, int])
                self.names_ages["someone"] = 20
                assert isinstance(self.names_ages["someone"], int)

        m = AttributeModule()
        # m will contain two attributes
        # 1. foo of type float
        # 2. names_ages of type Dict[str, int]

    .. testcleanup::

        del AttributeModule
        del m

        value: An initial value to be assigned to attribute.
        type: A Python type

        Returns `value`

def _get_type_trace_db():
    # This is a private API. Use of this for external purposes is discouraged.
    return type_trace_db

# Gets a function from the name of a method on a type
def _get_function_from_type(cls, name):
    return getattr(cls, name, None)

# ScriptClasses must be new-style classes because we construct them using their
# __new__ method.
def _is_new_style_class(cls):
    if hasattr(cls, "__class__"):
        return "__dict__" in dir(cls) or hasattr(cls, "__slots__")

# These OrderedDictWrapper classes replace the actual OrderedDicts in
# module with versions that get/set properties inside of Module.
# This allows us to reuse most of nn.Module while still storing the
# data in C++.
# Each OrderedDict needs to support:
#  x not in view
#  x in view
#  view[name] = ...
#  view.values()
#  del view[name]
#  view.items()
#  view.keys()
#  len(view)

class OrderedDictWrapper(object):
    def __init__(self, _c):
        self._c = _c

    def keys(self):
        return [k for k, v in self.items()]

    def values(self):
        return [v for k, v in self.items()]

    def __len__(self):
        return len(self.values())

    def __delitem__(self, k):
        raise RuntimeError("cannot delete methods or parameters of a script module")

    def items(self):
        return self._c.items()

    def __setitem__(self, k, v):
        if k not in self:
            raise RuntimeError(
                "Can't add a new parameter after ScriptModule construction."
                " Tried to add '{}".format(k)
        self._c.setattr(k, v)

    def __contains__(self, k):
        return self._c.contains(k)

    def __getitem__(self, k):
        if k not in self:
            raise KeyError(k)
        return self._c.getattr(k)

class OrderedModuleDict(OrderedDictWrapper):
    def __init__(self, module, python_dict):
        super(OrderedModuleDict, self).__init__(torch._C.ModuleDict(module))
        # contains _both_ script modules and non-script python-only modules

        # because script modules are subclassed in python and the
        # C++ Module class will not hold references to them,
        # to ensure that you always get the same python value here
        # we store it in the python dict as well
        self._python_modules = python_dict

    def items(self):
        r = self._python_modules.items()
        return r

    def __contains__(self, k):
        return k in self._python_modules

    def __setitem__(self, k, v):
        # Cases where sub-module can be re-assigned after ScriptModule construction
        # 1. If the attr is an module interface type, it's guaranteed that the module is
        #    not inlined in the graph, so it's safe to swap a new ScriptModule in.
        # 2. if the new value if a ScriptModule with the same JIT type, IR won't change
        #    and it's legit to swap a new module in.
        # In these two cases we allow swapping a new scripted module and update the
        # corresponding python module dict to keep sync.
        # Note: the value to be swapped in has to be ScriptModule instead of nn.Module,
        # otherwise it's illegal and we throw error.
        if isinstance(v, ScriptModule):
            self._c.setattr(k, v)
            self._python_modules[k] = v
            raise RuntimeError(
                "Cannot re-assign modules in a ScriptModule with non-scripted "
                "module, tried to replace existing module '{}': {}".format(k, v)

    def __getitem__(self, k):
        return self._python_modules[k]

# For each user-defined class that subclasses ScriptModule, this meta-class:
# (1) finds all the methods annotated with @script_method in a ScriptModule and
#     removes them from the class attributes
# (2) puts a wrapper around the class's __init__ method to recursively compile
#     all of the script_methods with the module after the original __init__ has
#     run. This has to occur after the user-defined __init__ so that submodules and
#     parameters are initialized _before_ the script compiler resolve references to
#     `self.param` or `self.module`.
class ScriptMeta(type):
    def __init__(cls, name, bases, attrs):  # noqa: B902
        # Aggregate all the ScriptMethods and constants from superclasses
        cls._methods: Dict[str, Any] = {}
        cls._constants_set = set(getattr(cls, "__constants__", ()))
        for base in reversed(bases):
            for k, v in getattr(base, "_methods", {}).items():
                cls._methods[k] = v
            base_constants = getattr(base, "_constants_set", set())
            cls._constants_set = cls._constants_set.union(base_constants)

        # find all the script methods of the current class
        for k, v in sorted(attrs.items()):
            if isinstance(v, ScriptMethodStub):
                delattr(cls, k)
                cls._methods[v.original_method.__name__] = v

        if getattr(cls, "_disable_script_meta", False):
            # We leave built-in ScriptModule types alone, since this metaclass
            # is only for compiling user classes that inherit from
            # ScriptModule.
            return super(ScriptMeta, cls).__init__(name, bases, attrs)

        original_init = getattr(cls, "__init__", lambda self: None)

        def init_then_script(self, *args, **kwargs):
            num_methods = len(cls._methods)
            original_init(self, *args, **kwargs)
            added_methods_in_init = len(cls._methods) > num_methods

            if type(self) == cls:

                def make_stubs(module):
                    cls = type(module)
                    if hasattr(cls, "_methods"):
                        return [v for k, v in sorted(cls._methods.items())]
                        return infer_methods_to_compile(module)

                ] = torch.jit._recursive.create_script_module(self, make_stubs, share_types=not added_methods_in_init)

                # Delete the Python attributes that now shadow the ScriptModule
                # ones, so that __getattr__ and __setattr__ will properly find
                # the scripted versions.
                concrete_type = self._actual_script_module._concrete_type
                for name in concrete_type.get_attributes():
                    delattr(self, name)
                for name, _ in concrete_type.get_modules():
                    delattr(self, name)
                for name in ("_parameters", "_buffers", "_modules"):
                    delattr(self, name)

        cls.__init__ = init_then_script  # type: ignore[misc]
        super(ScriptMeta, cls).__init__(name, bases, attrs)

class _CachedForward(object):
    def __get__(self, obj, cls):
        return self.__getattr__("forward")  # type: ignore[attr-defined]

class ScriptWarning(Warning):

def script_method(fn):
    if not _enabled:
        return fn
    # NOTE: we need to traverse two frames here because the meta-class frame
    # for ScriptModule will be present, as opposed to invoking @script on a
    # a function or invoking define() on a CompilationUnit.
    # The stack will look like:
    # 0. createResolutionCallback()
    # 1. script_method()
    # 2. ScriptModule metaclass frame
    # 3. Surrounding scope
    # createResolutionCallback internally adds 1 to get us to the scope of this
    # function (the calling function). Adding 2 gets us to the proper surrounding scope.
    _rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=2)
    ast = get_jit_def(fn, fn.__name__, self_name="ScriptModule")
    return ScriptMethodStub(_rcb, ast, fn)

class ConstMap:
    def __init__(self, const_mapping):
        self.const_mapping = const_mapping

    def __getattr__(self, attr):
        return self.const_mapping[attr]

def unpackage_script_module(importer: PackageImporter, script_module_id: str) -> torch.nn.Module:
    Called by ``torch.package.PackageImporter``'s Pickler's ``persistent_load`` function.
    Performs work of loading and returning a ScriptModule from a ``torch.package`` archive.
    if not isinstance(importer.zip_reader, torch._C.PyTorchFileReader):
        raise RuntimeError(
            "Loading ScriptObjects from a PackageImporter created from a "
            "directory is not supported. Use a package archive file instead."
    cu = torch._C.CompilationUnit()
    cpp_module = torch._C._import_ir_module_from_package(
    return wrap_cpp_module(cpp_module)

if _enabled:
    _magic_methods = [

    class RecursiveScriptClass(object):
        An analogue of RecursiveScriptModule for regular objects that are not modules.
        This class is a wrapper around a torch._C.ScriptObject that represents an instance
        of a TorchScript class and allows it to be used in Python.

            _c [torch._C.ScriptObject]: The C++ object to which attribute lookups and method
                calls are forwarded.
            _props [Dict[str, property]]: A dictionary of properties fetched from self._c and
                exposed on this wrppaer.
        def __init__(self, cpp_class):
            super(RecursiveScriptClass, self).__init__()
            self.__dict__["_initializing"] = True
            self._c = cpp_class

            # Add wrapped object's properties to this class instance.
            self._props = { property(prop.getter, prop.setter) for prop in self._c._properties()}

            self.__dict__["_initializing"] = False

        def __getattr__(self, attr):
            if "_initializing" in self.__dict__ and self.__dict__["_initializing"]:
                return super(RecursiveScriptClass, self).__getattr__(attr)  # type: ignore[misc]

            if attr in self._props:
                return self._props[attr].fget()

            return getattr(self._c, attr)

        def __setattr__(self, attr, value):
            if "_initializing" in self.__dict__ and self.__dict__["_initializing"]:
                return super(RecursiveScriptClass, self).__setattr__(attr, value)

            if attr in self._props:
                return self._props[attr].fset(value)

            setattr(self._c, attr, value)

        # Delegate calls to magic methods like __len__ to the C++ module backing the
        # RecursiveScriptClass.
        def forward_magic_method(self, method_name, *args, **kwargs):
            if not self._c._has_method(method_name):
                raise TypeError()

            self_method = self.__getattr__(method_name)
            return self_method(*args, **kwargs)

        def __getstate__(self):
            raise pickle.PickleError("ScriptClasses cannot be pickled")

        def __iadd__(self, other):
            if self._c._has_method("__iadd__"):
                return self.forward_magic_method("__iadd__", other)
                return self.forward_magic_method("__add__", other)

    for method_name in _magic_methods:
        def method_template(self, *args, **kwargs):
            return self.forward_magic_method(method_name, *args, **kwargs)

        setattr(RecursiveScriptClass, method_name, method_template)

    # this is a Python 'non-data descriptor' that causes the first access
    # to ScriptModule's forward to look up the forward method and stash
    # it in the objects dict. Due to the standard rules for attribute lookup,
    # subsequent lookups will just directly return the previously looked up method.
    # This is necessary because nn.Module defines forward as a method. If we
    # did nothing, __getattr__ would not be called. Instead we'd get nn.Module.forward
    # which always throws an exception.

    class ScriptModule(with_metaclass(ScriptMeta, Module)):  # type: ignore[misc]
        A wrapper around C++ ``torch::jit::Module``. ``ScriptModule``\s
        contain methods, attributes, parameters, and
        constants. These can be accessed the same way as on a normal ``nn.Module``.
        __jit_unused_properties__ = ['code', 'code_with_constants', 'graph', 'inlined_graph', 'original_name']

        def __init__(self):
            super(ScriptModule, self).__init__()

        forward = _CachedForward()

        def __getattr__(self, attr):
            if "_actual_script_module" not in self.__dict__:
                return super(ScriptModule, self).__getattr__(attr)
            return getattr(self._actual_script_module, attr)

        def __setattr__(self, attr, value):
            if "_actual_script_module" not in self.__dict__:
                # Unwrap torch.jit.Attribute into a regular setattr + record
                # the provided type in __annotations__.
                # This ensures that if we use the attr again in `__init__`, it
                # will look like the actual value, not an instance of Attribute.
                if isinstance(value, Attribute):
                    # NB: Ensure that we set __annotations__ on the specific
                    # class in question, and not on a superclass (which would
                    # be wrong wrong wrong!).
                    # See also
                    if "__annotations__" not in self.__class__.__dict__:
                        self.__class__.__annotations__ = {}
                    self.__annotations__[attr] = value.type
                    value = value.value
                return super(ScriptModule, self).__setattr__(attr, value)

            setattr(self._actual_script_module, attr, value)

        def define(self, src):
            if "_actual_script_module" in self.__dict__:
                # If we have completed initialization, just defer to the
                # backing RecursiveScriptModule to eagerly compile the provided
                # source.
                return self._actual_script_module.define(src)

            # Otherwise, we are still in the object's __init__.
            # In that case, add `src` as a stub to be compiled.
            # We use frames_up=1 to get to the proper surrounding scope. The stack
            # will look like:
            # 0. createResolutionCallback
            # 1. define()
            # 2. surrounding scope.
            # createResolutionCallback internally adds 1 to get us to our frame, then
            # we add 1 to get to the proper surrounding scope.
            rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
            ast = torch._C._parse_source_def(src)
            self._methods[] = ScriptMethodStub(rcb, ast, None)

        def _replicate_for_data_parallel(self):
            return self._actual_script_module._replicate_for_data_parallel()

        def __reduce_package__(self, exporter: PackageExporter):
            Called by ``torch.package.PackageExporter``'s Pickler's ``persistent_id`` when
            saving TorchScript objects. Performs act of saving a ScriptModule inside of
            a ``torch.package`` archive.

            Returns method to load the ScriptModule from a ``torch.package.PackageImporter``'s
            Pickler's ``persistent_load`` function.
            script_module_id = exporter.get_unique_id()
            exporter.script_module_serializer.serialize(self._c, int(script_module_id))
            return (unpackage_script_module, (script_module_id,))

    class RecursiveScriptModule(ScriptModule):
        # XXX: RecursiveScriptModule inherits from ScriptModule for the sole
        # reason that it retains the existing isinstance(ScriptModule)
        # behavior.
        The core data structure in TorchScript is the ``ScriptModule``. It is an
        analogue of torch's ``nn.Module`` and represents an entire model as a tree of
        submodules. Like normal modules, each individual module in a ``ScriptModule`` can
        have submodules, parameters, and methods. In ``nn.Module``\s methods are implemented
        as Python functions, but in ``ScriptModule``\s methods are implemented as
        TorchScript functions, a statically-typed subset of Python that contains all
        of PyTorch's built-in Tensor operations. This difference allows your
        ``ScriptModule``\s code to run without the need for a Python interpreter.

        ``ScriptModule``\s should not be created manually, instead use
        either :func:`tracing <torch.jit.trace>` or :func:`scripting <torch.jit.script>`.
        Tracing and scripting can be applied incrementally and :ref:`composed as necessary <Types>`.

        * Tracing records the tensor operations as executed with a set of example inputs and uses these
          operations to construct a computation graph. You can use the full dynamic behavior of Python with tracing,
          but values other than Tensors and control flow aren't captured in the graph.

        * Scripting inspects the Python code of the model
          and compiles it to TorchScript. Scripting allows the use of many `types`_ of values and supports dynamic control flow.
          Many, but not all features of Python are supported by the compiler, so changes to the source code may be necessary.
        _disable_script_meta = True

        def __init__(self, cpp_module):
            self.__dict__["_initializing"] = True
            self._c = cpp_module
            super(RecursiveScriptModule, self).__init__()
            # Delete the 'training' attribute set up by `Module.__init__`. It
            # will get set on the underlying cpp module, so we delete it here
            # to avoid this version shadowing the cpp module version.
            delattr(self, "training")

        def _construct(cpp_module, init_fn):
            Construct a RecursiveScriptModule that's ready for use. PyTorch
            code should use this to construct a RecursiveScriptModule instead
            of instead of calling `__init__` directly, as it makes sure the
            object is properly finalized (and in the future, we may take
            control of how the RecursiveScriptModule instance is created).

                cpp_module:  The C++ Module that will hold the actual state of
                             this RecursiveScriptModule instance.
                init_fn:  Lambda that initializes the RecursiveScriptModule passed to it.
            script_module = RecursiveScriptModule(cpp_module)

            # Finalize the ScriptModule: replace the nn.Module state with our
            # custom implementations and flip the _initializing bit.
            return script_module

        def _finalize_scriptmodule(script_module):
            script_module._parameters = OrderedDictWrapper(
            script_module._buffers = OrderedDictWrapper(
            script_module._modules = OrderedModuleDict(
                script_module._c, script_module._modules
            script_module._initializing = False

        def _reconstruct(self, cpp_module):
            Re-construct an instance of RecursiveScriptModule using an instance of a C++ module.

                cpp_module: The C++ module that this RecursiveScriptModule will be rebuilt around.
            self.__init__(cpp_module)  # type: ignore[misc]

            # Copy the concrete type from the C++ module to this ScriptModule.
            self._concrete_type = torch._C.ConcreteModuleType.from_jit_type(

            # Copy submodules from the C++ module to this ScriptModule.
            modules = {}
            for name, cpp_module in torch._C.ModuleDict(self._c).items():
                modules[name] = wrap_cpp_module(cpp_module)
            self._modules = OrderedModuleDict(self._c, modules)

            # Copy parameters and buffers.
            self._parameters = OrderedDictWrapper(torch._C.ParameterDict(self._c))
            self._buffers = OrderedDictWrapper(torch._C.BufferDict(self._c))

            # Get rid of the functions from the old C++ module.
            self.__dict__ = {
                k: v
                for k, v in self.__dict__.items()
                if not isinstance(v, torch._C.ScriptMethod)
            self.__dict__["_initializing"] = False

        def graph(self):
            Returns a string representation of the internal graph for the
            ``forward`` method. See :ref:`interpreting-graphs` for details.
            return self._c._get_method("forward").graph

        def inlined_graph(self):
            Returns a string representation of the internal graph for the
            ``forward`` method. This graph will be preprocessed to inline all function and method calls.
            See :ref:`interpreting-graphs` for details.
            return self.forward.inlined_graph

        def code(self):
            Returns a pretty-printed representation (as valid Python syntax) of
            the internal graph for the ``forward`` method. See
            :ref:`inspecting-code` for details.
            return self.forward.code

        def code_with_constants(self):
            Returns a tuple of:

            [0] a pretty-printed representation (as valid Python syntax) of
            the internal graph for the ``forward`` method. See `code`.
            [1] a ConstMap following the CONSTANT.cN format of the output in [0].
            The indices in the [0] output are keys to the underlying constant's values.

            See :ref:`inspecting-code` for details.
            r = self.forward.code_with_constants
            return (r[0], ConstMap(r[1]))

        def save(self, f, **kwargs):
            save(f, _extra_files={})

            See :func:` <>` for details.
            return, **kwargs)

        def _save_for_lite_interpreter(self, *args, **kwargs):

            Add (or update) the bytecode session to the script model. The updated model is used
            in lite interpreter for mobile applications.

                f: a string containing a file name.
                _extra_files: Map from filename to contents which will be stored as part of 'f'.

            return self._c._save_for_mobile(*args, **kwargs)

        def _save_to_buffer_for_lite_interpreter(self, *args, **kwargs):
            return self._c._save_to_buffer_for_mobile(*args, **kwargs)

        def save_to_buffer(self, *args, **kwargs):
            return self._c.save_to_buffer(*args, **kwargs)

        def get_debug_state(self, *args, **kwargs):
            return self._c.get_debug_state()

        def extra_repr(self):
            return "original_name={}".format(self.original_name)

        def graph_for(self, *args, **kwargs):
            return self.forward.graph_for(*args, **kwargs)

        def original_name(self):
            if type(self) == str(self._c._type().name()):
                return ""
            return str(self._c._type().name())

        def define(self, src):
            # We use frames_up=1 to get to the proper surrounding scope. The stack
            # will look like:
            # 0. createResolutionCallback
            # 1. define()
            # 2. surrounding scope.
            # createResolutionCallback internally adds 1 to get us to our frame, then
            # we add 1 to get to the proper surrounding scope.
            rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
            self._c._define(self._concrete_type, src, rcb)

        def __getattr__(self, attr):
            if "_initializing" not in self.__dict__:
                raise RuntimeError(
                    "ScriptModule has not been initialized, did you forget to call super's init?"

            if self._initializing:
                return super(RecursiveScriptModule, self).__getattr__(attr)

            # _modules check is before hasattr since modules are included as attributes in _c,
            # but we want to get the python wrapper from _modules instead of the raw _c object.
            if attr in self._modules:
                return self._modules[attr]
            elif self._c.hasattr(attr):
                return self._c.getattr(attr)
            elif self._c._has_method(attr):
                script_method = self._c._get_method(attr)
                # cache method so future calls do not go through __getattr__
                # to improve invocation performance
                self.__dict__[attr] = script_method
                return script_method

            return super(RecursiveScriptModule, self).__getattr__(attr)

        def __setattr__(self, attr, value):
            if self._initializing:
                return super(RecursiveScriptModule, self).__setattr__(attr, value)

            if attr in self._modules:
                self._modules[attr] = value
            elif self._c.hasattr(attr):
                self._c.setattr(attr, value)
            elif (
                hasattr(self, "_concrete_type")
                and attr in self._concrete_type.get_constants().keys()
                # TODO: we don't have _concrete_type set after load(), and in general we lose constant information.
                # We should encode constants as class type attributes (or something) so it persists across save/load.
                raise AttributeError(
                    "Cannot mutate TorchScript constant value: '{}'. Value: '{}'".format(
                        attr, value
                # We allow setting Python attributes on the ScriptModule, for
                # when people want to stash some convenience info on it.
                # TODO: it's possible that the following is confusing:
                #   s = torch.jit.script(...)
                #   s.python_attr = ...
                #   <--- this doesn't have `python_attr`
                # It's fairly trivial to save enough info to warn in this case.
                return super(RecursiveScriptModule, self).__setattr__(attr, value)

        def __copy__(self):
            return torch.jit._recursive.wrap_cpp_module(copy.copy(self._c))

        def __deepcopy__(self, memo):
            return torch.jit._recursive.wrap_cpp_module(copy.deepcopy(self._c, memo))

        # Python magic methods do method lookups on an object's class type, instead of looking up
        # the method defines on the class instance. In order to continue to expose the magic methods
        # of builtin-containers (ModuleList, Sequential, ModuleDict) to Python, we
        # define magic methods here as a shim to the correct attribute.
        def forward_magic_method(self, method_name, *args, **kwargs):
            self_method = getattr(self, method_name)
            if getattr(self_method, "__func__", None) == getattr(
                RecursiveScriptModule, method_name
                raise NotImplementedError()
            return self_method(*args, **kwargs)

        def __iter__(self):
            return self.forward_magic_method("__iter__")

        def __getitem__(self, idx):
            return self.forward_magic_method("__getitem__", idx)

        def __len__(self):
            return self.forward_magic_method("__len__")

        def __contains__(self, key):
            return self.forward_magic_method("__contains__", key)

        # dir is defined by the base nn.Module, so instead of throwing if
        # it is not overridden, we call into the nn.Module __dir__ method
        def __dir__(self):
            self_method = self.__dir__
            if self_method.__func__ == _get_function_from_type(  # type: ignore[attr-defined]
                RecursiveScriptModule, "__dir__"
                return super(RecursiveScriptModule, self).__dir__()
            return self_method()

        # to resolve bool(value), Python looks if __bool__ is defined then __iter__
        # is defined then returns true for classes. Since __iter__() on this
        # class throws if it isn't overridden, we define __bool__ to preserve default behavior
        def __bool__(self):
            self_method = self.__bool__
            if self_method.__func__ == _get_function_from_type(  # type: ignore[attr-defined]
                RecursiveScriptModule, "__bool__"
                return True
            return self_method()

        def _replicate_for_data_parallel(self):
            # we have to initialize ScriptModule properly so that
            # it works with pybind11
            def init_fn(script_module):
                # Don't do anything here, we'll initialize the ScriptModule below

            return RecursiveScriptModule._construct(
                self._c._replicate_for_data_parallel(), init_fn

    # Need to copy all RecursiveScriptModule methods to ScriptModule.
    # This is because `super(MyScriptModule, self).foo()` does not use
    # `__getattr__` to look up `foo`. So we need to make each method available on
    # the ScriptModule manually.
    for name, item in RecursiveScriptModule.__dict__.items():
        if not callable(item) and not isinstance(item, property):
        if name.startswith("__") or hasattr(ScriptModule, name):
        # We can copy over the implementation wholesale because besides the
        # `super()` thing above, ScriptModule behaves exactly like
        # RecursiveScriptModule
        setattr(ScriptModule, name, item)

    def _get_methods(cls):
        import inspect

        # In Python 3 unbound methods are functions, but in Python 2 they are methods
        return inspect.getmembers(
            cls, predicate=lambda x: inspect.isfunction(x) or inspect.ismethod(x)

    _compiled_methods_allowlist = {

    def _make_fail(name):
        def fail(self, *args, **kwargs):
            raise RuntimeError(name + " is not supported on ScriptModules")

        return fail

    for name, method in _get_methods(torch.nn.Module):
        if name.startswith("__"):
        if (
            name not in RecursiveScriptModule.__dict__
            and name not in _compiled_methods_allowlist
            setattr(RecursiveScriptModule, method.__name__, _make_fail(name))

    class RecursiveScriptClass(object):  # type: ignore[no-redef]
        def __init__(self):

[docs]    class ScriptModule(torch.nn.Module):  # type: ignore[no-redef]
        def __init__(self, arg=None):

    class RecursiveScriptModule(ScriptModule):  # type: ignore[no-redef]
        def __init__(self, arg=None):

def call_prepare_scriptable_func_impl(obj, memo):
    if not isinstance(obj, torch.nn.Module):
        return obj

    obj_id = id(obj)

    # If obj_id is in memo, obj has already been prepared or is being
    # prepared in another call up the stack.
    if obj_id in memo:
        return memo[id(obj)]

    obj = obj.__prepare_scriptable__() if hasattr(obj, '__prepare_scriptable__') else obj  # type: ignore[operator]
    # Record obj in memo to avoid infinite recursion in the case of cycles in the module
    # hierarchy when recursing below.
    memo[obj_id] = obj

    new_obj_dict = {}

    for name, sub_module in obj.__dict__.items():
        if name == '_modules':
            for k, v in sub_module.items():
                sub_module[k] = call_prepare_scriptable_func_impl(v, memo)
            new_obj_dict[name] = sub_module
        elif isinstance(sub_module, torch.nn.Module) and not isinstance(sub_module, ScriptModule):
            new_obj_dict[name] = call_prepare_scriptable_func_impl(sub_module, memo)
            new_obj_dict[name] = sub_module

    for k, v in new_obj_dict.items():
        obj.__dict__[name] = v

    return obj

def call_prepare_scriptable_func(obj):
    memo: Dict[int, torch.nn.Module] = {}
    return call_prepare_scriptable_func_impl(obj, memo)

def create_script_dict(obj):
    Create a ``torch._C.ScriptDict`` instance with the data from ``obj``.

        obj (dict): The Python dictionary that is used to initialize the ``ScriptDict``
                    returned by this function.

        An instance of ``torch._C.ScriptDict`` that has the same data as ``obj``
        and can be passed between Python and TorchScript with reference semantics and
        zero copy overhead.
    return torch._C.ScriptDict(obj)  # type: ignore[attr-defined]

def create_script_list(obj, type_hint=None):
    Create a ``torch._C.ScriptList`` instance with the data from ``obj``.
        obj (dict): The Python list that is used to initialize the ``ScriptList``
                    returned by this function.
        An instance of ``torch._C.ScriptList`` that has the same data as ``obj``
        and can be passed between Python and TorchScript with reference semantics and
        zero copy overhead.
    return torch._C.ScriptList(obj)  # type: ignore[attr-defined]

[docs]def script(obj, optimize=None, _frames_up=0, _rcb=None,
           example_inputs: Union[List[Tuple], Dict[Callable, List[Tuple]], None] = None):
    Scripting a function or ``nn.Module`` will inspect the source code, compile
    it as TorchScript code using the TorchScript compiler, and return a :class:`ScriptModule` or
    :class:`ScriptFunction`. TorchScript itself is a subset of the Python language, so not all
    features in Python work, but we provide enough functionality to compute on
    tensors and do control-dependent operations. For a complete guide, see the

    Scripting a dictionary or list copies the data inside it into a TorchScript instance than can be
    subsequently passed by reference between Python and TorchScript with zero copy overhead.

    ``torch.jit.script`` can be used as a function for modules, functions, dictionaries and lists
     and as a decorator ``@torch.jit.script`` for :ref:`torchscript-classes` and functions.

        obj (callable, class, or ``nn.Module``):  The ``nn.Module``, function, class type,
                                                  dictionary, or list to compile.
        example_inputs (Union[List[Tuple], Dict[Callable, List[Tuple]], None]): Provide example inputs
            to annotate the arguments for a function or ``nn.Module``.

        If ``obj`` is ``nn.Module``, ``script`` returns
        a :class:`ScriptModule` object. The returned :class:`ScriptModule` will
        have the same set of sub-modules and parameters as the
        original ``nn.Module``. If ``obj`` is a standalone function,
        a :class:`ScriptFunction` will be returned. If ``obj`` is a ``dict``, then
        ``script`` returns an instance of `torch._C.ScriptDict`. If ``obj`` is a ``list``,
        then ``script`` returns an instance of `torch._C.ScriptList`.

    **Scripting a function**
        The ``@torch.jit.script`` decorator will construct a :class:`ScriptFunction`
        by compiling the body of the function.

        Example (scripting a function):

        .. testcode::

            import torch

            def foo(x, y):
                if x.max() > y.max():
                    r = x
                    r = y
                return r

            print(type(foo))  # torch.jit.ScriptFunction

            # See the compiled graph as Python code

            # Call the function using the TorchScript interpreter
            foo(torch.ones(2, 2), torch.ones(2, 2))

        .. testoutput::


    ****Scripting a function using example_inputs**
        Example inputs can be used to annotate a function arguments.

        Example (annotating a function before scripting):

        .. testcode::

            import torch

            def test_sum(a, b):
                return a + b

            # Annotate the arguments to be int
            scripted_fn = torch.jit.script(test_sum, example_inputs=[(3, 4)])

            print(type(scripted_fn))  # torch.jit.ScriptFunction

            # See the compiled graph as Python code

            # Call the function using the TorchScript interpreter
            scripted_fn(20, 100)

        .. testoutput::


    **Scripting an nn.Module**
        Scripting an ``nn.Module`` by default will compile the ``forward`` method and recursively
        compile any methods, submodules, and functions called by ``forward``. If a ``nn.Module`` only uses
        features supported in TorchScript, no changes to the original module code should be necessary. ``script``
        will construct :class:`ScriptModule` that has copies of the attributes, parameters, and methods of
        the original module.

        Example (scripting a simple module with a Parameter):

        .. testcode::

            import torch

            class MyModule(torch.nn.Module):
                def __init__(self, N, M):
                    super(MyModule, self).__init__()
                    # This parameter will be copied to the new ScriptModule
                    self.weight = torch.nn.Parameter(torch.rand(N, M))

                    # When this submodule is used, it will be compiled
                    self.linear = torch.nn.Linear(N, M)

                def forward(self, input):
                    output =

                    # This calls the `forward` method of the `nn.Linear` module, which will
                    # cause the `self.linear` submodule to be compiled to a `ScriptModule` here
                    output = self.linear(output)
                    return output

            scripted_module = torch.jit.script(MyModule(2, 3))

        Example (scripting a module with traced submodules):

        .. testcode::

            import torch
            import torch.nn as nn
            import torch.nn.functional as F

            class MyModule(nn.Module):
                def __init__(self):
                    super(MyModule, self).__init__()
                    # torch.jit.trace produces a ScriptModule's conv1 and conv2
                    self.conv1 = torch.jit.trace(nn.Conv2d(1, 20, 5), torch.rand(1, 1, 16, 16))
                    self.conv2 = torch.jit.trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16))

                def forward(self, input):
                    input = F.relu(self.conv1(input))
                    input = F.relu(self.conv2(input))
                    return input

            scripted_module = torch.jit.script(MyModule())

        To compile a method other than ``forward`` (and recursively compile anything it calls), add
        the :func:`@torch.jit.export <torch.jit.export>` decorator to the method. To opt out of compilation
        use :func:`@torch.jit.ignore <torch.jit.ignore>` or :func:`@torch.jit.unused <torch.jit.unused>`.

        Example (an exported and ignored method in a module)::

            import torch
            import torch.nn as nn

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

                def some_entry_point(self, input):
                    return input + 10

                def python_only_fn(self, input):
                    # This function won't be compiled, so any
                    # Python APIs can be used
                    import pdb

                def forward(self, input):
                    return input * 99

            scripted_module = torch.jit.script(MyModule())
            print(scripted_module.some_entry_point(torch.randn(2, 2)))
            print(scripted_module(torch.randn(2, 2)))

        Example ( Annotating forward of nn.Module using example_inputs)::

            import torch
            import torch.nn as nn
            from typing import NamedTuple

            class MyModule(NamedTuple):
            result: List[int]

            class TestNNModule(torch.nn.Module):
                def forward(self, a) -> MyModule:
                    result = MyModule(result=a)
                    return result

            pdt_model = TestNNModule()

            # Runs the pdt_model in eager model with the inputs provided and annotates the arguments of forward
            scripted_model = torch.jit.script(pdt_model, example_inputs={pdt_model: [([10, 20, ], ), ], })

            # Run the scripted_model with actual inputs
    global type_trace_db
    if not _enabled:
        return obj

    if optimize is not None:
            "`optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead"

    # No-op for modules, functions, class instances that are already scripted
    if isinstance(obj, RecursiveScriptClass):
        return obj
    if isinstance(obj, ScriptModule):
        return obj
    if isinstance(obj, ScriptFunction):
        return obj

    if example_inputs:
        # If MonkeyType is installed, enable profile directed type annotation
        # Check if example_inputs are defined and generate call traces
        # for the method by running eager mode version of the method with
        # the provide example inputs. This logs all the traces in type_trace_db
        type_trace_db = JitTypeTraceStore()
        if monkeytype_trace:
            monkeytype_config = JitTypeTraceConfig(type_trace_db)
            with monkeytype_trace(monkeytype_config):
                if isinstance(example_inputs, Dict):
                    # If the obj is an nn.Module or a class, then each method is
                    # executed with the arguments provided in the example inputs.
                    # example inputs here will be of type Dict(class.method, (arguments))
                    # This is used to infer type annotations for those methods
                    # which are not called directly under the hood of monkeytype.
                    for module, example_input in example_inputs.items():
                        for example in example_input:
                elif isinstance(example_inputs, List):
                    for examples in example_inputs:
                    raise ValueError("Error: Unable to infer types. Please format the inputs to type `List[Tuple]`"
                                     " or `Dict[Callable, List[Tuple]]` to be run with MonkeyType.")
            warnings.warn("Warning: monkeytype is not installed. Please install "
                          "to enable Profile-Directed Typing in TorchScript. Refer to "
                          " to install MonkeyType. ")

    if isinstance(obj, torch.nn.Module):
        obj = call_prepare_scriptable_func(obj)
        return torch.jit._recursive.create_script_module(
            obj, torch.jit._recursive.infer_methods_to_compile

    if isinstance(obj, dict):
        return create_script_dict(obj)
    if isinstance(obj, list):
        return create_script_list(obj)

    if inspect.isclass(obj):
        qualified_name = _qualified_name(obj)
        # If this type is a `nn.Module` subclass, they probably meant to pass
        # an instance instead of a Module
        if issubclass(obj, torch.nn.Module):
            raise RuntimeError(
                "Type '{}' cannot be compiled since it inherits"
                " from nn.Module,"
                " pass an instance instead".format(obj)

        # Enums are automatically usable in TorchScript, explicitly scripting
        # is not necessary, but not harmful either.
        if issubclass(obj, enum.Enum):
            return obj

        if not _is_new_style_class(obj):
            raise RuntimeError(
                "TorchScript classes must be new-style classes. "
                "Please inherit from 'object'."
        if len(obj.mro()) > 2:
            raise RuntimeError(
                "TorchScript classes does not support inheritance yet. "
                "Please directly inherit from 'object'."
        if _rcb is None:
            _rcb = _jit_internal.createResolutionCallbackFromFrame(_frames_up + 1)
        _compile_and_register_class(obj, _rcb, qualified_name)
        return obj
    elif inspect.isfunction(obj) or inspect.ismethod(obj):
        qualified_name = _qualified_name(obj)
        # this is a decorated fn, and we need to the underlying fn and its rcb
        if hasattr(obj, "__script_if_tracing_wrapper"):
            obj = obj.__original_fn
            _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)

        maybe_already_compiled_fn = _try_get_jit_cached_function(obj)
        if maybe_already_compiled_fn:
            return maybe_already_compiled_fn
        ast = get_jit_def(obj, obj.__name__)
        if _rcb is None:
            _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
        fn = torch._C._jit_script_compile(
            qualified_name, ast, _rcb, get_default_args(obj)
        # Forward docstrings
        fn.__doc__ = obj.__doc__
        _set_jit_function_cache(obj, fn)
        return fn
        return torch.jit._recursive.create_script_class(obj)

# overloads are registered in _jit_internal and compiled here so that _overload
# can be used in nn/ without an import cycle

def _check_overload_defaults(impl_defaults, overload_defaults, loc):
    for name, overload_value in overload_defaults.items():
        if name not in impl_defaults or impl_defaults[name] != overload_value:
            raise torch.jit.frontend.FrontendError(
                "Default parameters on overloads do not affect the runtime so they "
                "must equal to the default parameter on the implementation function. Found on "
                "parameter {name}".format(name=name),

def _compile_function_with_overload(overload_fn, qual_name, impl_fn):
    overload_decl = get_jit_def(overload_fn, overload_fn.__name__).decl()
    overload_signature = torch.jit.annotations.get_signature(
        overload_fn, None, None, inspect.ismethod(overload_fn)
    impl_ast = get_jit_def(impl_fn, impl_fn.__name__)
    overload_defaults = get_default_args(overload_fn)
    implementation_defaults = get_default_args(impl_fn)
    _rcb = _jit_internal.createResolutionCallbackFromClosure(impl_fn)
        implementation_defaults, overload_defaults, overload_decl.range()
    fn = torch._C._jit_script_compile_overload(
    return fn

def _get_overloads(obj):
    # check for cached compiled fns
    existing_compiled_fns = _try_get_jit_cached_overloads(obj)
    qual_name = _qualified_name(obj)
    uncompiled_overloads = _jit_internal._get_fn_overloads(qual_name)
    if uncompiled_overloads is None:
        return existing_compiled_fns

    if obj in uncompiled_overloads:
        raise RuntimeError(_jit_internal.get_overload_no_implementation_error_message(
            'function', obj))

    compiled_fns = []
    for overload_fn in uncompiled_overloads:
            _compile_function_with_overload(overload_fn, qual_name, obj)

    if existing_compiled_fns:
        compiled_fns = existing_compiled_fns + compiled_fns

    # cache compilation, remove information stored to do compilation
    _set_jit_overload_cache(obj, compiled_fns)
    return compiled_fns

def _check_directly_compile_overloaded(obj):
    qual_name = _qualified_name(obj)
    if _jit_internal._get_fn_overloads(qual_name) or _try_get_jit_cached_overloads(obj):
        raise RuntimeError(
            "Function {} cannot be directly compiled because it"
            " is overloaded. It must be used in a context of a function"
            " where its inputs can determine which overload to call.".format(qual_name)

def interface(obj):
    if not inspect.isclass(obj):
        raise RuntimeError("interface must be applied to a class")
    if not _is_new_style_class(obj):
        raise RuntimeError("TorchScript interfaces must inherit from 'object'")

    # Expected MRO is:
    #   User module
    #   torch.nn.modules.module.Module
    #   object
    is_module_interface = issubclass(obj, torch.nn.Module) and len(obj.mro()) == 3

    if not is_module_interface and len(obj.mro()) > 2:
        raise RuntimeError(
            "TorchScript interface does not support inheritance yet. "
            "Please directly inherit from 'object' or 'nn.Module'."

    qualified_name = _qualified_name(obj)
    rcb = _jit_internal.createResolutionCallbackFromFrame(1)
    # if this type is a `nn.Module` subclass, generate a module interface type
    # instead of a class interface type; a module interface type only compiles
    # the user provided methods as part of the interface
    ast = get_jit_class_def(obj, obj.__name__)
    mangled_classname = torch._C._jit_script_interface_compile(
        qualified_name, ast, rcb, is_module_interface
    obj.__torch_script_interface__ = mangled_classname
    return obj

def _recursive_compile_class(obj, loc):
    _qual_name = _qualified_name(obj)
    # We're starting a new compilation, so update the error call stack in
    # case it fails
    error_stack = torch._C.CallStack(_qual_name, loc)
    rcb = _jit_internal.createResolutionCallbackForClassMethods(obj)
    return _compile_and_register_class(obj, rcb, _qual_name)

CompilationUnit = torch._C.CompilationUnit
set_module(CompilationUnit, "torch.jit")

def pad(s: str, padding: int, offset: int = 0, char: str = ' '):
    if padding >= len(s):
        padding -= len(s)
    return ''.join([char for _ in range(padding + offset)]) + s

class _ScriptProfileColumn:
    def __init__(self, header: str, alignment: int = 4, offset: int = 0):
        self.header = header
        self.alignment = alignment
        self.offset = offset
        self.rows: Dict[int, Any] = {}

    def add_row(self, lineno: int, value: Any):
        self.rows[lineno] = value

    def materialize(self):
        max_length = len(self.header)
        rows: List[Tuple[int, str]] = []
        for (key, value) in self.rows.items():
            cell = str(value)
            rows.append((key, cell))
            max_length = max(len(cell), max_length)

        if self.alignment > 0:
            padding = max_length + self.alignment
            padding -= padding % self.alignment
            padding = 0

        rows = [(key, pad(cell, padding, self.offset)) for key, cell in rows]
        return pad(self.header, padding, self.offset), rows

class _ScriptProfileTable:
    def __init__(self, cols: List[_ScriptProfileColumn], source_range: List[int]):
        self.cols = cols
        self.source_range = source_range

    def dump_string(self):
        outputs: List[str] = []
        cells: List[Tuple[str, Dict[int, str]]] = []
        header_buffer = ''
        for col in self.cols:
            header, rows = col.materialize()
            header_buffer += header
            cells.append((header, dict(rows)))

        outputs.append(pad('', len(header_buffer), 0, '='))
        for line in self.source_range:
            row_buffer = ''
            for header, rows in cells:
                cell = rows.get(line)
                if cell is None:
                    row_buffer += pad('', len(header))
                    row_buffer += cell
        return '\n'.join(outputs)

class _ScriptProfile:
    def __init__(self):
        self.profile = classes.profiling._ScriptProfile()

    def enable(self):

    def disable(self):

    def dump_string(self) -> str:
        outputs: List[str] = []
        for source_stats in self.profile._dump_stats():
            source_ref = source_stats.source()
            source_lines = source_ref.text().splitlines()
            dedent = min([len(line) - len(line.lstrip(' ')) for line in source_lines])
            source_lines = [line[dedent:] for line in source_lines]

            start_line = source_ref.starting_lineno()
            end_line = start_line + len(source_lines)
            source_range = range(start_line, end_line)
            lineno = _ScriptProfileColumn("Line #")
            hits = _ScriptProfileColumn("Hits")
            time_ns = _ScriptProfileColumn("Time (ns)")
            line_contents = _ScriptProfileColumn("Line Contents", 0, 1)
            stats = source_stats.line_map()
            for line in source_range:
                lineno.add_row(line, line)
                line_contents.add_row(line, source_lines[line - start_line])
                stat = stats.get(line)
                if stat is not None:
                    hits.add_row(line, stat.count())
                    time_ns.add_row(line, stat.duration_ns())

            table = _ScriptProfileTable([lineno, hits, time_ns, line_contents], list(source_range))
        return '\n\n'.join(outputs)

    def dump(self):

def _unwrap_optional(x):
    assert x is not None, "Unwrapping null optional"
    return x

_register_builtin(_unwrap_optional, "aten::_unwrap_optional")
_register_builtin(_jit_internal.is_scripting, "aten::is_scripting")
_register_builtin(has_torch_function, "aten::has_torch_function")
_register_builtin(has_torch_function_unary, "aten::has_torch_function")
_register_builtin(has_torch_function_variadic, "aten::has_torch_function")


Go go.mod file reference module Notes

Go go.mod file reference module Examples

Go go.mod file reference module Syntax

Go go.mod file reference Example

Go go.mod file reference

Go Publishing a module Publishing steps

Go Developing a major version update Branching for a major release

Go Module release and versioning workflow Publishing breaking API changes

Go Module release and versioning workflow Publishing the first stable version

Go Module release and versioning workflow Publishing the first (unstable) version

Go Module release and versioning workflow Publishing pre-release versions

Go Contribution Guide Miscellaneous topics Sending multiple dependent changes

Go Contribution Guide Miscellaneous topics Set up git aliases

Go Contribution Guide Miscellaneous topics Reviewing code by others

Go Contribution Guide Miscellaneous topics Synchronize your client

Go Contribution Guide Miscellaneous topics Specifying a reviewer / CCing others

Go Contribution Guide Miscellaneous topics Quickly testing your changes

Go Contribution Guide Miscellaneous topics Troubleshooting mail errors

Go Contribution Guide Miscellaneous topics Copyright headers

Contribution Guide Good commit messages

Go Contribution Guide Sending a change via Gerrit Step 5: Revise changes after a review

Go Contribution Guide Sending a change via Gerrit Step 4: Send changes for review

Go Contribution Guide Sending a change via Gerrit Step 3: Test your changes In the repositories

Contribution Guide Sending a change via Gerrit Step 3: Test your changes In the main Go repository

Go Contribution Guide Sending a change via Gerrit Step 2: Prepare changes in a new branch

Go Contribution Guide Sending a change via Gerrit Step 1: Clone the source code

Go Contribution Guide Sending a change via Gerrit Overview

Go Contribution Guide Becoming a contributor Step 4: Install the git-codereview command

Contribution Guide Becoming a contributor Step 0: Select a Google Account

Go Contribution Guide Becoming a contributor Overview

Go Diagnostics Debugging

Go Diagnostics Profiling

How to Write Go Code Testing

How to Write Go Code Your first program Importing packages from remote modules

How to Write Go Code Your first program Importing packages from your module

How to Write Go Code Your first program

The Go Programming Language Specification System considerations Size and alignment guarantees

The Go Programming Language Specification System considerations Package unsafe

The Go Programming Language Specification Run-time panics

The Go Programming Language Specification Errors

The Go Programming Language Specification Program initialization and execution Program execution

The Go Programming Language Specification Program initialization and execution Package initialization

The Go Programming Language Specification Program initialization and execution The zero value

The Go Programming Language Specification Packages An example package

The Go Programming Language Specification Packages Import declarations

The Go Programming Language Specification Packages Package clause