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llava/lib/python3.10/site-packages/setuptools/_core_metadata.py

@@ -0,0 +1,321 @@
+"""
+Handling of Core Metadata for Python packages (including reading and writing).
+
+See: https://packaging.python.org/en/latest/specifications/core-metadata/
+"""
+
+from __future__ import annotations
+
+import os
+import stat
+import textwrap
+from email import message_from_file
+from email.message import Message
+from tempfile import NamedTemporaryFile
+
+from packaging.markers import Marker
+from packaging.requirements import Requirement
+from packaging.utils import canonicalize_name, canonicalize_version
+from packaging.version import Version
+
+from . import _normalization, _reqs
+from ._static import is_static
+from .warnings import SetuptoolsDeprecationWarning
+
+from distutils.util import rfc822_escape
+
+
+def get_metadata_version(self):
+    mv = getattr(self, 'metadata_version', None)
+    if mv is None:
+        mv = Version('2.2')
+        self.metadata_version = mv
+    return mv
+
+
+def rfc822_unescape(content: str) -> str:
+    """Reverse RFC-822 escaping by removing leading whitespaces from content."""
+    lines = content.splitlines()
+    if len(lines) == 1:
+        return lines[0].lstrip()
+    return '\n'.join((lines[0].lstrip(), textwrap.dedent('\n'.join(lines[1:]))))
+
+
+def _read_field_from_msg(msg: Message, field: str) -> str | None:
+    """Read Message header field."""
+    value = msg[field]
+    if value == 'UNKNOWN':
+        return None
+    return value
+
+
+def _read_field_unescaped_from_msg(msg: Message, field: str) -> str | None:
+    """Read Message header field and apply rfc822_unescape."""
+    value = _read_field_from_msg(msg, field)
+    if value is None:
+        return value
+    return rfc822_unescape(value)
+
+
+def _read_list_from_msg(msg: Message, field: str) -> list[str] | None:
+    """Read Message header field and return all results as list."""
+    values = msg.get_all(field, None)
+    if values == []:
+        return None
+    return values
+
+
+def _read_payload_from_msg(msg: Message) -> str | None:
+    value = str(msg.get_payload()).strip()
+    if value == 'UNKNOWN' or not value:
+        return None
+    return value
+
+
+def read_pkg_file(self, file):
+    """Reads the metadata values from a file object."""
+    msg = message_from_file(file)
+
+    self.metadata_version = Version(msg['metadata-version'])
+    self.name = _read_field_from_msg(msg, 'name')
+    self.version = _read_field_from_msg(msg, 'version')
+    self.description = _read_field_from_msg(msg, 'summary')
+    # we are filling author only.
+    self.author = _read_field_from_msg(msg, 'author')
+    self.maintainer = None
+    self.author_email = _read_field_from_msg(msg, 'author-email')
+    self.maintainer_email = None
+    self.url = _read_field_from_msg(msg, 'home-page')
+    self.download_url = _read_field_from_msg(msg, 'download-url')
+    self.license = _read_field_unescaped_from_msg(msg, 'license')
+
+    self.long_description = _read_field_unescaped_from_msg(msg, 'description')
+    if self.long_description is None and self.metadata_version >= Version('2.1'):
+        self.long_description = _read_payload_from_msg(msg)
+    self.description = _read_field_from_msg(msg, 'summary')
+
+    if 'keywords' in msg:
+        self.keywords = _read_field_from_msg(msg, 'keywords').split(',')
+
+    self.platforms = _read_list_from_msg(msg, 'platform')
+    self.classifiers = _read_list_from_msg(msg, 'classifier')
+
+    # PEP 314 - these fields only exist in 1.1
+    if self.metadata_version == Version('1.1'):
+        self.requires = _read_list_from_msg(msg, 'requires')
+        self.provides = _read_list_from_msg(msg, 'provides')
+        self.obsoletes = _read_list_from_msg(msg, 'obsoletes')
+    else:
+        self.requires = None
+        self.provides = None
+        self.obsoletes = None
+
+    self.license_files = _read_list_from_msg(msg, 'license-file')
+
+
+def single_line(val):
+    """
+    Quick and dirty validation for Summary pypa/setuptools#1390.
+    """
+    if '\n' in val:
+        # TODO: Replace with `raise ValueError("newlines not allowed")`
+        # after reviewing #2893.
+        msg = "newlines are not allowed in `summary` and will break in the future"
+        SetuptoolsDeprecationWarning.emit("Invalid config.", msg)
+        # due_date is undefined. Controversial change, there was a lot of push back.
+        val = val.strip().split('\n')[0]
+    return val
+
+
+def write_pkg_info(self, base_dir):
+    """Write the PKG-INFO file into the release tree."""
+    temp = ""
+    final = os.path.join(base_dir, 'PKG-INFO')
+    try:
+        # Use a temporary file while writing to avoid race conditions
+        # (e.g. `importlib.metadata` reading `.egg-info/PKG-INFO`):
+        with NamedTemporaryFile("w", encoding="utf-8", dir=base_dir, delete=False) as f:
+            temp = f.name
+            self.write_pkg_file(f)
+        permissions = stat.S_IMODE(os.lstat(temp).st_mode)
+        os.chmod(temp, permissions | stat.S_IRGRP | stat.S_IROTH)
+        os.replace(temp, final)  # atomic operation.
+    finally:
+        if temp and os.path.exists(temp):
+            os.remove(temp)
+
+
+# Based on Python 3.5 version
+def write_pkg_file(self, file):  # noqa: C901  # is too complex (14)  # FIXME
+    """Write the PKG-INFO format data to a file object."""
+    version = self.get_metadata_version()
+
+    def write_field(key, value):
+        file.write(f"{key}: {value}\n")
+
+    write_field('Metadata-Version', str(version))
+    write_field('Name', self.get_name())
+    write_field('Version', self.get_version())
+
+    summary = self.get_description()
+    if summary:
+        write_field('Summary', single_line(summary))
+
+    optional_fields = (
+        ('Home-page', 'url'),
+        ('Download-URL', 'download_url'),
+        ('Author', 'author'),
+        ('Author-email', 'author_email'),
+        ('Maintainer', 'maintainer'),
+        ('Maintainer-email', 'maintainer_email'),
+    )
+
+    for field, attr in optional_fields:
+        attr_val = getattr(self, attr, None)
+        if attr_val is not None:
+            write_field(field, attr_val)
+
+    license = self.get_license()
+    if license:
+        write_field('License', rfc822_escape(license))
+
+    for label, url in self.project_urls.items():
+        write_field('Project-URL', f'{label}, {url}')
+
+    keywords = ','.join(self.get_keywords())
+    if keywords:
+        write_field('Keywords', keywords)
+
+    platforms = self.get_platforms() or []
+    for platform in platforms:
+        write_field('Platform', platform)
+
+    self._write_list(file, 'Classifier', self.get_classifiers())
+
+    # PEP 314
+    self._write_list(file, 'Requires', self.get_requires())
+    self._write_list(file, 'Provides', self.get_provides())
+    self._write_list(file, 'Obsoletes', self.get_obsoletes())
+
+    # Setuptools specific for PEP 345
+    if hasattr(self, 'python_requires'):
+        write_field('Requires-Python', self.python_requires)
+
+    # PEP 566
+    if self.long_description_content_type:
+        write_field('Description-Content-Type', self.long_description_content_type)
+
+    self._write_list(file, 'License-File', self.license_files or [])
+    _write_requirements(self, file)
+
+    for field, attr in _POSSIBLE_DYNAMIC_FIELDS.items():
+        if (val := getattr(self, attr, None)) and not is_static(val):
+            write_field('Dynamic', field)
+
+    long_description = self.get_long_description()
+    if long_description:
+        file.write(f"\n{long_description}")
+        if not long_description.endswith("\n"):
+            file.write("\n")
+
+
+def _write_requirements(self, file):
+    for req in _reqs.parse(self.install_requires):
+        file.write(f"Requires-Dist: {req}\n")
+
+    processed_extras = {}
+    for augmented_extra, reqs in self.extras_require.items():
+        # Historically, setuptools allows "augmented extras": `<extra>:<condition>`
+        unsafe_extra, _, condition = augmented_extra.partition(":")
+        unsafe_extra = unsafe_extra.strip()
+        extra = _normalization.safe_extra(unsafe_extra)
+
+        if extra:
+            _write_provides_extra(file, processed_extras, extra, unsafe_extra)
+        for req in _reqs.parse_strings(reqs):
+            r = _include_extra(req, extra, condition.strip())
+            file.write(f"Requires-Dist: {r}\n")
+
+    return processed_extras
+
+
+def _include_extra(req: str, extra: str, condition: str) -> Requirement:
+    r = Requirement(req)  # create a fresh object that can be modified
+    parts = (
+        f"({r.marker})" if r.marker else None,
+        f"({condition})" if condition else None,
+        f"extra == {extra!r}" if extra else None,
+    )
+    r.marker = Marker(" and ".join(x for x in parts if x))
+    return r
+
+
+def _write_provides_extra(file, processed_extras, safe, unsafe):
+    previous = processed_extras.get(safe)
+    if previous == unsafe:
+        SetuptoolsDeprecationWarning.emit(
+            'Ambiguity during "extra" normalization for dependencies.',
+            f"""
+            {previous!r} and {unsafe!r} normalize to the same value:\n
+                {safe!r}\n
+            In future versions, setuptools might halt the build process.
+            """,
+            see_url="https://peps.python.org/pep-0685/",
+        )
+    else:
+        processed_extras[safe] = unsafe
+        file.write(f"Provides-Extra: {safe}\n")
+
+
+# from pypa/distutils#244; needed only until that logic is always available
+def get_fullname(self):
+    return _distribution_fullname(self.get_name(), self.get_version())
+
+
+def _distribution_fullname(name: str, version: str) -> str:
+    """
+    >>> _distribution_fullname('setup.tools', '1.0-2')
+    'setup_tools-1.0.post2'
+    >>> _distribution_fullname('setup-tools', '1.2post2')
+    'setup_tools-1.2.post2'
+    >>> _distribution_fullname('setup-tools', '1.0-r2')
+    'setup_tools-1.0.post2'
+    >>> _distribution_fullname('setup.tools', '1.0.post')
+    'setup_tools-1.0.post0'
+    >>> _distribution_fullname('setup.tools', '1.0+ubuntu-1')
+    'setup_tools-1.0+ubuntu.1'
+    """
+    return "{}-{}".format(
+        canonicalize_name(name).replace('-', '_'),
+        canonicalize_version(version, strip_trailing_zero=False),
+    )
+
+
+_POSSIBLE_DYNAMIC_FIELDS = {
+    # Core Metadata Field x related Distribution attribute
+    "author": "author",
+    "author-email": "author_email",
+    "classifier": "classifiers",
+    "description": "long_description",
+    "description-content-type": "long_description_content_type",
+    "download-url": "download_url",
+    "home-page": "url",
+    "keywords": "keywords",
+    "license": "license",
+    # "license-file": "license_files", # XXX: does PEP 639 exempt Dynamic ??
+    "maintainer": "maintainer",
+    "maintainer-email": "maintainer_email",
+    "obsoletes": "obsoletes",
+    # "obsoletes-dist": "obsoletes_dist",  # NOT USED
+    "platform": "platforms",
+    "project-url": "project_urls",
+    "provides": "provides",
+    # "provides-dist": "provides_dist",  # NOT USED
+    "provides-extra": "extras_require",
+    "requires": "requires",
+    "requires-dist": "install_requires",
+    # "requires-external": "requires_external",  # NOT USED
+    "requires-python": "python_requires",
+    "summary": "description",
+    # "supported-platform": "supported_platforms",  # NOT USED
+}

llava/lib/python3.10/site-packages/setuptools/_path.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+import contextlib
+import os
+import sys
+from typing import TYPE_CHECKING, TypeVar, Union
+
+from more_itertools import unique_everseen
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+StrPath: TypeAlias = Union[str, os.PathLike[str]]  #  Same as _typeshed.StrPath
+StrPathT = TypeVar("StrPathT", bound=Union[str, os.PathLike[str]])
+
+
+def ensure_directory(path):
+    """Ensure that the parent directory of `path` exists"""
+    dirname = os.path.dirname(path)
+    os.makedirs(dirname, exist_ok=True)
+
+
+def same_path(p1: StrPath, p2: StrPath) -> bool:
+    """Differs from os.path.samefile because it does not require paths to exist.
+    Purely string based (no comparison between i-nodes).
+    >>> same_path("a/b", "./a/b")
+    True
+    >>> same_path("a/b", "a/./b")
+    True
+    >>> same_path("a/b", "././a/b")
+    True
+    >>> same_path("a/b", "./a/b/c/..")
+    True
+    >>> same_path("a/b", "../a/b/c")
+    False
+    >>> same_path("a", "a/b")
+    False
+    """
+    return normpath(p1) == normpath(p2)
+
+
+def normpath(filename: StrPath) -> str:
+    """Normalize a file/dir name for comparison purposes."""
+    # See pkg_resources.normalize_path for notes about cygwin
+    file = os.path.abspath(filename) if sys.platform == 'cygwin' else filename
+    return os.path.normcase(os.path.realpath(os.path.normpath(file)))
+
+
+@contextlib.contextmanager
+def paths_on_pythonpath(paths):
+    """
+    Add the indicated paths to the head of the PYTHONPATH environment
+    variable so that subprocesses will also see the packages at
+    these paths.
+
+    Do this in a context that restores the value on exit.
+
+    >>> getfixture('monkeypatch').setenv('PYTHONPATH', 'anything')
+    >>> with paths_on_pythonpath(['foo', 'bar']):
+    ...     assert 'foo' in os.environ['PYTHONPATH']
+    ...     assert 'anything' in os.environ['PYTHONPATH']
+    >>> os.environ['PYTHONPATH']
+    'anything'
+
+    >>> getfixture('monkeypatch').delenv('PYTHONPATH')
+    >>> with paths_on_pythonpath(['foo', 'bar']):
+    ...     assert 'foo' in os.environ['PYTHONPATH']
+    >>> os.environ.get('PYTHONPATH')
+    """
+    nothing = object()
+    orig_pythonpath = os.environ.get('PYTHONPATH', nothing)
+    current_pythonpath = os.environ.get('PYTHONPATH', '')
+    try:
+        prefix = os.pathsep.join(unique_everseen(paths))
+        to_join = filter(None, [prefix, current_pythonpath])
+        new_path = os.pathsep.join(to_join)
+        if new_path:
+            os.environ['PYTHONPATH'] = new_path
+        yield
+    finally:
+        if orig_pythonpath is nothing:
+            os.environ.pop('PYTHONPATH', None)
+        else:
+            os.environ['PYTHONPATH'] = orig_pythonpath

llava/lib/python3.10/site-packages/setuptools/_reqs.py

@@ -0,0 +1,42 @@
+from __future__ import annotations
+
+from collections.abc import Iterable, Iterator
+from functools import lru_cache
+from typing import TYPE_CHECKING, Callable, TypeVar, Union, overload
+
+import jaraco.text as text
+from packaging.requirements import Requirement
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+_T = TypeVar("_T")
+_StrOrIter: TypeAlias = Union[str, Iterable[str]]
+
+
+parse_req: Callable[[str], Requirement] = lru_cache()(Requirement)
+# Setuptools parses the same requirement many times
+# (e.g. first for validation than for normalisation),
+# so it might be worth to cache.
+
+
+def parse_strings(strs: _StrOrIter) -> Iterator[str]:
+    """
+    Yield requirement strings for each specification in `strs`.
+
+    `strs` must be a string, or a (possibly-nested) iterable thereof.
+    """
+    return text.join_continuation(map(text.drop_comment, text.yield_lines(strs)))
+
+
+# These overloads are only needed because of a mypy false-positive, pyright gets it right
+# https://github.com/python/mypy/issues/3737
+@overload
+def parse(strs: _StrOrIter) -> Iterator[Requirement]: ...
+@overload
+def parse(strs: _StrOrIter, parser: Callable[[str], _T]) -> Iterator[_T]: ...
+def parse(strs: _StrOrIter, parser: Callable[[str], _T] = parse_req) -> Iterator[_T]:  # type: ignore[assignment]
+    """
+    Replacement for ``pkg_resources.parse_requirements`` that uses ``packaging``.
+    """
+    return map(parser, parse_strings(strs))

llava/lib/python3.10/site-packages/setuptools/extension.py

@@ -0,0 +1,177 @@
+from __future__ import annotations
+
+import functools
+import re
+from typing import TYPE_CHECKING
+
+from setuptools._path import StrPath
+
+from .monkey import get_unpatched
+
+import distutils.core
+import distutils.errors
+import distutils.extension
+
+
+def _have_cython():
+    """
+    Return True if Cython can be imported.
+    """
+    cython_impl = 'Cython.Distutils.build_ext'
+    try:
+        # from (cython_impl) import build_ext
+        __import__(cython_impl, fromlist=['build_ext']).build_ext
+    except Exception:
+        return False
+    return True
+
+
+# for compatibility
+have_pyrex = _have_cython
+if TYPE_CHECKING:
+    # Work around a mypy issue where type[T] can't be used as a base: https://github.com/python/mypy/issues/10962
+    from distutils.core import Extension as _Extension
+else:
+    _Extension = get_unpatched(distutils.core.Extension)
+
+
+class Extension(_Extension):
+    """
+    Describes a single extension module.
+
+    This means that all source files will be compiled into a single binary file
+    ``<module path>.<suffix>`` (with ``<module path>`` derived from ``name`` and
+    ``<suffix>`` defined by one of the values in
+    ``importlib.machinery.EXTENSION_SUFFIXES``).
+
+    In the case ``.pyx`` files are passed as ``sources and`` ``Cython`` is **not**
+    installed in the build environment, ``setuptools`` may also try to look for the
+    equivalent ``.cpp`` or ``.c`` files.
+
+    :arg str name:
+      the full name of the extension, including any packages -- ie.
+      *not* a filename or pathname, but Python dotted name
+
+    :arg list[str|os.PathLike[str]] sources:
+      list of source filenames, relative to the distribution root
+      (where the setup script lives), in Unix form (slash-separated)
+      for portability.  Source files may be C, C++, SWIG (.i),
+      platform-specific resource files, or whatever else is recognized
+      by the "build_ext" command as source for a Python extension.
+
+    :keyword list[str] include_dirs:
+      list of directories to search for C/C++ header files (in Unix
+      form for portability)
+
+    :keyword list[tuple[str, str|None]] define_macros:
+      list of macros to define; each macro is defined using a 2-tuple:
+      the first item corresponding to the name of the macro and the second
+      item either a string with its value or None to
+      define it without a particular value (equivalent of "#define
+      FOO" in source or -DFOO on Unix C compiler command line)
+
+    :keyword list[str] undef_macros:
+      list of macros to undefine explicitly
+
+    :keyword list[str] library_dirs:
+      list of directories to search for C/C++ libraries at link time
+
+    :keyword list[str] libraries:
+      list of library names (not filenames or paths) to link against
+
+    :keyword list[str] runtime_library_dirs:
+      list of directories to search for C/C++ libraries at run time
+      (for shared extensions, this is when the extension is loaded).
+      Setting this will cause an exception during build on Windows
+      platforms.
+
+    :keyword list[str] extra_objects:
+      list of extra files to link with (eg. object files not implied
+      by 'sources', static library that must be explicitly specified,
+      binary resource files, etc.)
+
+    :keyword list[str] extra_compile_args:
+      any extra platform- and compiler-specific information to use
+      when compiling the source files in 'sources'.  For platforms and
+      compilers where "command line" makes sense, this is typically a
+      list of command-line arguments, but for other platforms it could
+      be anything.
+
+    :keyword list[str] extra_link_args:
+      any extra platform- and compiler-specific information to use
+      when linking object files together to create the extension (or
+      to create a new static Python interpreter).  Similar
+      interpretation as for 'extra_compile_args'.
+
+    :keyword list[str] export_symbols:
+      list of symbols to be exported from a shared extension.  Not
+      used on all platforms, and not generally necessary for Python
+      extensions, which typically export exactly one symbol: "init" +
+      extension_name.
+
+    :keyword list[str] swig_opts:
+      any extra options to pass to SWIG if a source file has the .i
+      extension.
+
+    :keyword list[str] depends:
+      list of files that the extension depends on
+
+    :keyword str language:
+      extension language (i.e. "c", "c++", "objc"). Will be detected
+      from the source extensions if not provided.
+
+    :keyword bool optional:
+      specifies that a build failure in the extension should not abort the
+      build process, but simply not install the failing extension.
+
+    :keyword bool py_limited_api:
+      opt-in flag for the usage of :doc:`Python's limited API <python:c-api/stable>`.
+
+    :raises setuptools.errors.PlatformError: if ``runtime_library_dirs`` is
+      specified on Windows. (since v63)
+    """
+
+    # These 4 are set and used in setuptools/command/build_ext.py
+    # The lack of a default value and risk of `AttributeError` is purposeful
+    # to avoid people forgetting to call finalize_options if they modify the extension list.
+    # See example/rationale in https://github.com/pypa/setuptools/issues/4529.
+    _full_name: str  #: Private API, internal use only.
+    _links_to_dynamic: bool  #: Private API, internal use only.
+    _needs_stub: bool  #: Private API, internal use only.
+    _file_name: str  #: Private API, internal use only.
+
+    def __init__(
+        self,
+        name: str,
+        sources: list[StrPath],
+        *args,
+        py_limited_api: bool = False,
+        **kw,
+    ) -> None:
+        # The *args is needed for compatibility as calls may use positional
+        # arguments. py_limited_api may be set only via keyword.
+        self.py_limited_api = py_limited_api
+        super().__init__(
+            name,
+            sources,  # type: ignore[arg-type] # Vendored version of setuptools supports PathLike
+            *args,
+            **kw,
+        )
+
+    def _convert_pyx_sources_to_lang(self):
+        """
+        Replace sources with .pyx extensions to sources with the target
+        language extension. This mechanism allows language authors to supply
+        pre-converted sources but to prefer the .pyx sources.
+        """
+        if _have_cython():
+            # the build has Cython, so allow it to compile the .pyx files
+            return
+        lang = self.language or ''
+        target_ext = '.cpp' if lang.lower() == 'c++' else '.c'
+        sub = functools.partial(re.sub, '.pyx$', target_ext)
+        self.sources = list(map(sub, self.sources))
+
+
+class Library(Extension):
+    """Just like a regular Extension, but built as a library instead"""

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_adaptive_avg_pool3d_backward.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_adaptive_avg_pool3d_backward_ops.h>
+
+namespace at {
+
+
+// aten::_adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor
+inline at::Tensor _adaptive_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self) {
+    return at::_ops::_adaptive_avg_pool3d_backward::call(grad_output, self);
+}
+
+// aten::_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _adaptive_avg_pool3d_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self) {
+    return at::_ops::_adaptive_avg_pool3d_backward_out::call(grad_output, self, out);
+}
+// aten::_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _adaptive_avg_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::_adaptive_avg_pool3d_backward_out::call(grad_output, self, out);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_add_relu_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _add_relu_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & _add_relu_outf(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_batch_norm_with_update.h

@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_batch_norm_with_update_ops.h>
+
+namespace at {
+
+
+// aten::_batch_norm_with_update(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor> _batch_norm_with_update(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, double momentum, double eps) {
+    return at::_ops::_batch_norm_with_update::call(input, weight, bias, running_mean, running_var, momentum, eps);
+}
+
+// aten::_batch_norm_with_update.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd, Tensor(g!) reserve) -> (Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _batch_norm_with_update_out(at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, at::Tensor & reserve, const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, double momentum, double eps) {
+    return at::_ops::_batch_norm_with_update_out::call(input, weight, bias, running_mean, running_var, momentum, eps, out, save_mean, save_invstd, reserve);
+}
+// aten::_batch_norm_with_update.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd, Tensor(g!) reserve) -> (Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _batch_norm_with_update_outf(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, at::Tensor & running_mean, at::Tensor & running_var, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, at::Tensor & reserve) {
+    return at::_ops::_batch_norm_with_update_out::call(input, weight, bias, running_mean, running_var, momentum, eps, out, save_mean, save_invstd, reserve);
+}
+
+// aten::_batch_norm_with_update_functional(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor, Tensor running_mean_out, Tensor running_var_out)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _batch_norm_with_update_functional(const at::Tensor & input, const ::std::optional<at::Tensor> & weight, const ::std::optional<at::Tensor> & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps) {
+    return at::_ops::_batch_norm_with_update_functional::call(input, weight, bias, running_mean, running_var, momentum, eps);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_cast_Byte_ops.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _cast_Byte {
+  using schema = at::Tensor (const at::Tensor &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_cast_Byte")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_cast_Byte(Tensor self, bool non_blocking=False) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, bool non_blocking);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_conj_copy_compositeexplicitautogradnonfunctional_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor _conj_copy(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_convert_indices_from_csr_to_coo_meta.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured__convert_indices_from_csr_to_coo : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose);
+};
+
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_convolution_mode_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor _convolution_mode(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation, int64_t groups);
+TORCH_API at::Tensor _convolution_mode_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups);
+
+} // namespace compositeimplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_cslt_sparse_mm_ops.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _cslt_sparse_mm {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const ::std::optional<at::Tensor> &, const ::std::optional<at::Tensor> &, ::std::optional<at::ScalarType>, bool, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_cslt_sparse_mm")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_cslt_sparse_mm(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False, int alg_id=0) -> Tensor")
+  static at::Tensor call(const at::Tensor & compressed_A, const at::Tensor & dense_B, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & alpha, ::std::optional<at::ScalarType> out_dtype, bool transpose_result, int64_t alg_id);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_A, const at::Tensor & dense_B, const ::std::optional<at::Tensor> & bias, const ::std::optional<at::Tensor> & alpha, ::std::optional<at::ScalarType> out_dtype, bool transpose_result, int64_t alg_id);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_fill_mem_eff_dropout_mask.h

@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_fill_mem_eff_dropout_mask_ops.h>
+
+namespace at {
+
+
+// aten::_fill_mem_eff_dropout_mask_(Tensor(a!) self, float dropout_p, int seed, int offset) -> Tensor(a!)
+inline at::Tensor & _fill_mem_eff_dropout_mask_(at::Tensor & self, double dropout_p, int64_t seed, int64_t offset) {
+    return at::_ops::_fill_mem_eff_dropout_mask_::call(self, dropout_p, seed, offset);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_sigmoid.h

@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_foreach_sigmoid_ops.h>
+
+namespace at {
+
+
+// aten::_foreach_sigmoid(Tensor[] self) -> Tensor[]
+inline ::std::vector<at::Tensor> _foreach_sigmoid(at::TensorList self) {
+    return at::_ops::_foreach_sigmoid::call(self);
+}
+
+// aten::_foreach_sigmoid_(Tensor(a!)[] self) -> ()
+inline void _foreach_sigmoid_(at::TensorList self) {
+    return at::_ops::_foreach_sigmoid_::call(self);
+}
+
+// aten::_foreach_sigmoid.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
+inline void _foreach_sigmoid_out(at::TensorList out, at::TensorList self) {
+    return at::_ops::_foreach_sigmoid_out::call(self, out);
+}
+// aten::_foreach_sigmoid.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
+inline void _foreach_sigmoid_outf(at::TensorList self, at::TensorList out) {
+    return at::_ops::_foreach_sigmoid_out::call(self, out);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_sigmoid_ops.h

@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _foreach_sigmoid {
+  using schema = ::std::vector<at::Tensor> (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_sigmoid")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_sigmoid(Tensor[] self) -> Tensor[]")
+  static ::std::vector<at::Tensor> call(at::TensorList self);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_sigmoid_ {
+  using schema = void (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_sigmoid_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_sigmoid_(Tensor(a!)[] self) -> ()")
+  static void call(at::TensorList self);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_sigmoid_out {
+  using schema = void (at::TensorList, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_sigmoid")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_sigmoid.out(Tensor[] self, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_zero_ops.h

@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _foreach_zero_ {
+  using schema = void (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_zero_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_zero_(Tensor(a!)[] self) -> ()")
+  static void call(at::TensorList self);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_zero_out {
+  using schema = void (at::TensorList, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_zero")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out);
+};
+
+struct TORCH_API _foreach_zero {
+  using schema = ::std::vector<at::Tensor> (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_zero")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_zero(Tensor[] self) -> Tensor[] self_out")
+  static ::std::vector<at::Tensor> call(at::TensorList self);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_has_same_storage_numel.h

@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_has_same_storage_numel_ops.h>
+
+namespace at {
+
+
+// aten::_has_same_storage_numel(Tensor self, Tensor other) -> bool
+inline bool _has_same_storage_numel(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::_has_same_storage_numel::call(self, other);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_linalg_det_meta.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured__linalg_det : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & A);
+};
+
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_from_padded_cpu_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor _nested_from_padded(const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213=false);
+
+} // namespace cpu
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_view_from_buffer.h

@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_nested_view_from_buffer_ops.h>
+
+namespace at {
+
+
+// aten::_nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor(a)
+inline at::Tensor _nested_view_from_buffer(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) {
+    return at::_ops::_nested_view_from_buffer::call(self, nested_size, nested_strides, offsets);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_scaled_mm_ops.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _scaled_mm {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, const at::Tensor &, const ::std::optional<at::Tensor> &, ::std::optional<at::ScalarType>, const ::std::optional<at::Tensor> &, const ::std::optional<at::Tensor> &, const ::std::optional<at::Tensor> &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_scaled_mm")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_scaled_mm(Tensor self, Tensor mat2, *, Tensor? bias=None, ScalarType? out_dtype=None, Tensor? scale_a=None, Tensor? scale_b=None, Tensor? scale_result=None, bool use_fast_accum=False) -> (Tensor, Tensor)")
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, const at::Tensor & mat2, const ::std::optional<at::Tensor> & bias, ::std::optional<at::ScalarType> out_dtype, const ::std::optional<at::Tensor> & scale_a, const ::std::optional<at::Tensor> & scale_b, const ::std::optional<at::Tensor> & scale_result, bool use_fast_accum);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, const ::std::optional<at::Tensor> & bias, ::std::optional<at::ScalarType> out_dtype, const ::std::optional<at::Tensor> & scale_a, const ::std::optional<at::Tensor> & scale_b, const ::std::optional<at::Tensor> & scale_result, bool use_fast_accum);
+};
+
+struct TORCH_API _scaled_mm_out {
+  using schema = ::std::tuple<at::Tensor &,at::Tensor &> (const at::Tensor &, const at::Tensor &, const ::std::optional<at::Tensor> &, ::std::optional<at::ScalarType>, const ::std::optional<at::Tensor> &, const ::std::optional<at::Tensor> &, const ::std::optional<at::Tensor> &, bool, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_scaled_mm")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_scaled_mm.out(Tensor self, Tensor mat2, *, Tensor? bias=None, ScalarType? out_dtype=None, Tensor? scale_a=None, Tensor? scale_b=None, Tensor? scale_result=None, bool use_fast_accum=False, Tensor(a!) out, Tensor(b!) out_amax) -> (Tensor(a!), Tensor(b!))")
+  static ::std::tuple<at::Tensor &,at::Tensor &> call(const at::Tensor & self, const at::Tensor & mat2, const ::std::optional<at::Tensor> & bias, ::std::optional<at::ScalarType> out_dtype, const ::std::optional<at::Tensor> & scale_a, const ::std::optional<at::Tensor> & scale_b, const ::std::optional<at::Tensor> & scale_result, bool use_fast_accum, at::Tensor & out, at::Tensor & out_amax);
+  static ::std::tuple<at::Tensor &,at::Tensor &> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, const ::std::optional<at::Tensor> & bias, ::std::optional<at::ScalarType> out_dtype, const ::std::optional<at::Tensor> & scale_a, const ::std::optional<at::Tensor> & scale_b, const ::std::optional<at::Tensor> & scale_result, bool use_fast_accum, at::Tensor & out, at::Tensor & out_amax);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_semi_structured_mm_native.h

@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor _sparse_semi_structured_mm(const at::Tensor & mat1, const at::Tensor & mat1_meta, const at::Tensor & mat2, ::std::optional<at::ScalarType> out_dtype=::std::nullopt);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_thnn_fused_gru_cell_backward_native.h

@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> _thnn_fused_gru_cell_backward_out(const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_fused_gru_cell_backward_cuda(const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_thnn_fused_lstm_cell_backward_native.h

@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> _thnn_fused_lstm_cell_backward(const ::std::optional<at::Tensor> & grad_hy, const ::std::optional<at::Tensor> & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/_upsample_bicubic2d_aa_cpu_dispatch.h

@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor _upsample_bicubic2d_aa(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor _upsample_bicubic2d_aa_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & _upsample_bicubic2d_aa_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & _upsample_bicubic2d_aa_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & _upsample_bicubic2d_aa_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & _upsample_bicubic2d_aa_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_max_pool2d_backward_ops.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API adaptive_max_pool2d_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::adaptive_max_pool2d_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "grad_input")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input);
+};
+
+struct TORCH_API adaptive_max_pool2d_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::adaptive_max_pool2d_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor")
+  static at::Tensor call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/alias_copy_ops.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API alias_copy {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::alias_copy")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "alias_copy(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API alias_copy_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::alias_copy")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "alias_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/argmax.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/argmax_ops.h>
+
+namespace at {
+
+
+// aten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
+inline at::Tensor argmax(const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false) {
+    return at::_ops::argmax::call(self, dim, keepdim);
+}
+
+// aten::argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & argmax_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false) {
+    return at::_ops::argmax_out::call(self, dim, keepdim, out);
+}
+// aten::argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & argmax_outf(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim, at::Tensor & out) {
+    return at::_ops::argmax_out::call(self, dim, keepdim, out);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/argmax_ops.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API argmax {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<int64_t>, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::argmax")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim);
+};
+
+struct TORCH_API argmax_out {
+  using schema = at::Tensor & (const at::Tensor &, ::std::optional<int64_t>, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::argmax")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim, at::Tensor & out);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/argmin_cuda_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor argmin(const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & argmin_out(at::Tensor & out, const at::Tensor & self, ::std::optional<int64_t> dim=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & argmin_outf(const at::Tensor & self, ::std::optional<int64_t> dim, bool keepdim, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/bitwise_not_ops.h

@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API bitwise_not {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::bitwise_not")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "bitwise_not(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API bitwise_not_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::bitwise_not_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "bitwise_not_(Tensor(a!) self) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API bitwise_not_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::bitwise_not")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/conv_tbc_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor conv_tbc(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad=0);
+TORCH_API at::Tensor & conv_tbc_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad=0);
+TORCH_API at::Tensor & conv_tbc_outf(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/convolution_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor convolution(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups);
+TORCH_API at::Tensor convolution_symint(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups);
+TORCH_API at::Tensor & convolution_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups);
+TORCH_API at::Tensor & convolution_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, at::Tensor & out);
+TORCH_API at::Tensor & convolution_symint_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups);
+TORCH_API at::Tensor & convolution_symint_outf(const at::Tensor & input, const at::Tensor & weight, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/cov_ops.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API cov {
+  using schema = at::Tensor (const at::Tensor &, int64_t, const ::std::optional<at::Tensor> &, const ::std::optional<at::Tensor> &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::cov")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "cov(Tensor self, *, int correction=1, Tensor? fweights=None, Tensor? aweights=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, int64_t correction, const ::std::optional<at::Tensor> & fweights, const ::std::optional<at::Tensor> & aweights);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t correction, const ::std::optional<at::Tensor> & fweights, const ::std::optional<at::Tensor> & aweights);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/diagonal_copy.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/diagonal_copy_ops.h>
+
+namespace at {
+
+
+// aten::diagonal_copy(Tensor self, int offset=0, int dim1=0, int dim2=1) -> Tensor
+inline at::Tensor diagonal_copy(const at::Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) {
+    return at::_ops::diagonal_copy::call(self, offset, dim1, dim2);
+}
+
+// aten::diagonal_copy.out(Tensor self, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & diagonal_copy_out(at::Tensor & out, const at::Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) {
+    return at::_ops::diagonal_copy_out::call(self, offset, dim1, dim2, out);
+}
+// aten::diagonal_copy.out(Tensor self, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & diagonal_copy_outf(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) {
+    return at::_ops::diagonal_copy_out::call(self, offset, dim1, dim2, out);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/diagonal_scatter_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & diagonal_scatter_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t offset=0, int64_t dim1=0, int64_t dim2=1);
+TORCH_API at::Tensor & diagonal_scatter_outf(const at::Tensor & self, const at::Tensor & src, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/erf_cpu_dispatch.h

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor erf(const at::Tensor & self);
+TORCH_API at::Tensor & erf_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & erf_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & erf_(at::Tensor & self);
+
+} // namespace cpu
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/fake_quantize_per_tensor_affine_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor fake_quantize_per_tensor_affine(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max);
+TORCH_API at::Tensor fake_quantize_per_tensor_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max);
+
+} // namespace compositeimplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/fft_ihfft2_native.h

@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor fft_ihfft2_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=::std::nullopt, at::IntArrayRef dim={-2,-1}, ::std::optional<c10::string_view> norm=::std::nullopt);
+TORCH_API const at::Tensor & fft_ihfft2_symint_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, ::std::optional<c10::string_view> norm, const at::Tensor & out);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/flip_compositeexplicitautograd_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & flip_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dims);
+TORCH_API at::Tensor & flip_outf(const at::Tensor & self, at::IntArrayRef dims, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/glu_backward_jvp_cuda_dispatch.h

@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor glu_backward_jvp(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim);
+
+} // namespace cuda
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/gradient.h

@@ -0,0 +1,60 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/gradient_ops.h>
+
+namespace at {
+
+
+// aten::gradient.scalarint(Tensor self, *, Scalar? spacing=None, int? dim=None, int edge_order=1) -> Tensor[]
+inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, const ::std::optional<at::Scalar> & spacing=::std::nullopt, ::std::optional<int64_t> dim=::std::nullopt, int64_t edge_order=1) {
+    return at::_ops::gradient_scalarint::call(self, spacing, dim, edge_order);
+}
+
+// aten::gradient.scalararray(Tensor self, *, Scalar spacing, int[] dim, int edge_order=1) -> Tensor[]
+inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, const at::Scalar & spacing, at::IntArrayRef dim, int64_t edge_order=1) {
+    return at::_ops::gradient_scalararray::call(self, spacing, dim, edge_order);
+}
+
+// aten::gradient.array(Tensor self, *, int[] dim, int edge_order=1) -> Tensor[]
+inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, at::IntArrayRef dim, int64_t edge_order=1) {
+    return at::_ops::gradient_array::call(self, dim, edge_order);
+}
+
+// aten::gradient.scalarrayint(Tensor self, *, Scalar[] spacing, int? dim=None, int edge_order=1) -> Tensor[]
+inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, at::ArrayRef<at::Scalar> spacing, ::std::optional<int64_t> dim=::std::nullopt, int64_t edge_order=1) {
+    return at::_ops::gradient_scalarrayint::call(self, spacing, dim, edge_order);
+}
+
+// aten::gradient.scalarrayarray(Tensor self, *, Scalar[] spacing, int[] dim, int edge_order=1) -> Tensor[]
+inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, at::ArrayRef<at::Scalar> spacing, at::IntArrayRef dim, int64_t edge_order=1) {
+    return at::_ops::gradient_scalarrayarray::call(self, spacing, dim, edge_order);
+}
+
+// aten::gradient.tensorarrayint(Tensor self, *, Tensor[] spacing, int? dim=None, int edge_order=1) -> Tensor[]
+inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, at::TensorList spacing, ::std::optional<int64_t> dim=::std::nullopt, int64_t edge_order=1) {
+    return at::_ops::gradient_tensorarrayint::call(self, spacing, dim, edge_order);
+}
+
+// aten::gradient.tensorarray(Tensor self, *, Tensor[] spacing, int[] dim, int edge_order=1) -> Tensor[]
+inline ::std::vector<at::Tensor> gradient(const at::Tensor & self, at::TensorList spacing, at::IntArrayRef dim, int64_t edge_order=1) {
+    return at::_ops::gradient_tensorarray::call(self, spacing, dim, edge_order);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/grid_sampler_2d_backward_native.h

@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> grid_sampler_2d_backward_out(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask, at::Tensor & out0, at::Tensor & out1);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> grid_sampler_2d_backward_cpu(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> grid_sampler_2d_backward_cuda(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array<bool,2> output_mask);
+} // namespace native
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/gru_cell_ops.h

@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API gru_cell {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const ::std::optional<at::Tensor> &, const ::std::optional<at::Tensor> &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::gru_cell")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional<at::Tensor> & b_ih, const ::std::optional<at::Tensor> & b_hh);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const ::std::optional<at::Tensor> & b_ih, const ::std::optional<at::Tensor> & b_hh);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/hardsigmoid_compositeexplicitautogradnonfunctional_dispatch.h

@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor hardsigmoid(const at::Tensor & self);
+TORCH_API at::Tensor & hardsigmoid_(at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/hardsigmoid_cpu_dispatch.h

@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor hardsigmoid(const at::Tensor & self);
+TORCH_API at::Tensor & hardsigmoid_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & hardsigmoid_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & hardsigmoid_(at::Tensor & self);
+
+} // namespace cpu
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/hstack.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/hstack_ops.h>
+
+namespace at {
+
+
+// aten::hstack(Tensor[] tensors) -> Tensor
+inline at::Tensor hstack(at::TensorList tensors) {
+    return at::_ops::hstack::call(tensors);
+}
+
+// aten::hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hstack_out(at::Tensor & out, at::TensorList tensors) {
+    return at::_ops::hstack_out::call(tensors, out);
+}
+// aten::hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hstack_outf(at::TensorList tensors, at::Tensor & out) {
+    return at::_ops::hstack_out::call(tensors, out);
+}
+
+}

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/linalg_eig_cpu_dispatch.h

@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> linalg_eig(const at::Tensor & self);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> linalg_eig_out(at::Tensor & eigenvalues, at::Tensor & eigenvectors, const at::Tensor & self);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> linalg_eig_outf(const at::Tensor & self, at::Tensor & eigenvalues, at::Tensor & eigenvectors);
+
+} // namespace cpu
+} // namespace at

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/linalg_vecdot_ops.h

@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API linalg_vecdot {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::linalg_vecdot")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "linalg_vecdot(Tensor x, Tensor y, *, int dim=-1) -> Tensor")
+  static at::Tensor call(const at::Tensor & x, const at::Tensor & y, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & y, int64_t dim);
+};
+
+struct TORCH_API linalg_vecdot_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::linalg_vecdot")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "linalg_vecdot.out(Tensor x, Tensor y, *, int dim=-1, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & x, const at::Tensor & y, int64_t dim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & y, int64_t dim, at::Tensor & out);
+};
+
+}} // namespace at::_ops

parrot/lib/python3.10/site-packages/torch/include/ATen/ops/log_softmax.h

@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/log_softmax_ops.h>
+
+namespace at {
+
+
+// aten::log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
+inline at::Tensor log_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::log_softmax_int::call(self, dim, dtype);
+}
+
+// aten::log_softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & log_softmax_out(at::Tensor & out, const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::log_softmax_int_out::call(self, dim, dtype, out);
+}
+// aten::log_softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & log_softmax_outf(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
+    return at::_ops::log_softmax_int_out::call(self, dim, dtype, out);
+}
+
+// aten::log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor
+inline at::Tensor log_softmax(const at::Tensor & self, at::Dimname dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::log_softmax_Dimname::call(self, dim, dtype);
+}
+
+}