#
# PyTorch documentation build configuration file, created by
# sphinx-quickstart on Fri Dec 23 13:31:47 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.

# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os

# import sys
import pkgutil
import re
from os import path

# source code directory, relative to this file, for sphinx-autobuild
# sys.path.insert(0, os.path.abspath('../..'))
import torch


try:
    import torchvision  # noqa: F401
except ImportError:
    import warnings

    warnings.warn('unable to load "torchvision" package')

RELEASE = os.environ.get("RELEASE", False)

import pytorch_sphinx_theme


# -- General configuration ------------------------------------------------

# If your documentation needs a minimal Sphinx version, state it here.
#
needs_sphinx = "3.1.2"

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
    "sphinx.ext.autodoc",
    "sphinx.ext.autosummary",
    "sphinx.ext.doctest",
    "sphinx.ext.intersphinx",
    "sphinx.ext.todo",
    "sphinx.ext.coverage",
    "sphinx.ext.napoleon",
    "sphinx.ext.viewcode",
    "sphinxcontrib.katex",
    "sphinx.ext.autosectionlabel",
    "sphinx_copybutton",
    "sphinx_panels",
    "myst_parser",
]

# build the templated autosummary files
autosummary_generate = True
numpydoc_show_class_members = False

# Theme has bootstrap already
panels_add_bootstrap_css = False

# autosectionlabel throws warnings if section names are duplicated.
# The following tells autosectionlabel to not throw a warning for
# duplicated section names that are in different documents.
autosectionlabel_prefix_document = True

# katex options
#
#

katex_prerender = True

napoleon_use_ivar = True

# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]

# TODO: document these and remove them from here.

coverage_ignore_functions = [
    # torch
    "typename",
    # torch.cuda
    "check_error",
    "cudart",
    "is_bf16_supported",
    # torch.cuda._sanitizer
    "zip_arguments",
    "zip_by_key",
    # torch.distributed.autograd
    "is_available",
    # torch.distributed.checkpoint.state_dict
    "gc_context",
    "state_dict",
    # torch.distributed.elastic.events
    "construct_and_record_rdzv_event",
    "record_rdzv_event",
    # torch.distributed.elastic.metrics
    "initialize_metrics",
    # torch.distributed.elastic.rendezvous.registry
    "get_rendezvous_handler",
    # torch.distributed.launch
    "launch",
    "main",
    "parse_args",
    # torch.distributed.rpc
    "is_available",
    # torch.distributed.run
    "config_from_args",
    "determine_local_world_size",
    "get_args_parser",
    "get_rdzv_endpoint",
    "get_use_env",
    "main",
    "parse_args",
    "parse_min_max_nnodes",
    "run",
    "run_script_path",
    # torch.distributions.constraints
    "is_dependent",
    # torch.hub
    "import_module",
    # torch.jit
    "export_opnames",
    # torch.jit.unsupported_tensor_ops
    "execWrapper",
    # torch.onnx
    "unregister_custom_op_symbolic",
    # torch.ao.quantization
    "default_eval_fn",
    # torch.backends
    "disable_global_flags",
    "flags_frozen",
    # torch.distributed.algorithms.ddp_comm_hooks
    "register_ddp_comm_hook",
    # torch.nn
    "factory_kwargs",
    # torch.nn.parallel
    "DistributedDataParallelCPU",
    # torch.utils
    "set_module",
    # torch.utils.model_dump
    "burn_in_info",
    "get_info_and_burn_skeleton",
    "get_inline_skeleton",
    "get_model_info",
    "get_storage_info",
    "hierarchical_pickle",
    # torch.amp.autocast_mode
    "autocast_decorator",
    # torch.ao.nn.quantized.dynamic.modules.rnn
    "apply_permutation",
    "pack_weight_bias",
    # torch.ao.nn.quantized.reference.modules.rnn
    "get_quantized_weight",
    # torch.ao.ns.fx.graph_matcher
    "get_matching_subgraph_pairs",
    # torch.ao.ns.fx.graph_passes
    "add_loggers_to_model",
    "create_a_shadows_b",
    # torch.ao.ns.fx.mappings
    "add_op_to_sets_of_related_ops",
    "get_base_name_for_op",
    "get_base_name_to_sets_of_related_ops",
    "get_node_type_to_io_type_map",
    "get_unmatchable_types_map",
    # torch.ao.ns.fx.n_shadows_utils
    "create_add_loggers_graph",
    "create_n_transformed_and_logged_copies_of_subgraph",
    "create_one_transformed_and_logged_copy_of_subgraph",
    "create_results_comparison",
    "create_submodule_from_subgraph",
    "extract_weight_comparison",
    "group_results_by_subgraph",
    "print_n_shadows_summary",
    # torch.ao.ns.fx.pattern_utils
    "end_node_matches_reversed_fusion",
    "get_reversed_fusions",
    "get_type_a_related_to_b",
    # torch.ao.ns.fx.utils
    "get_arg_indices_of_inputs_to_log",
    "get_node_first_input_and_output_type",
    "get_node_input_qparams",
    "get_normalized_nth_input",
    "get_number_of_non_param_args",
    "get_target_type_str",
    "maybe_add_missing_fqns",
    "maybe_dequantize_first_two_tensor_args_and_handle_tuples",
    "op_type_supports_shadowing",
    "rekey_logger_info_on_node_name_of_model",
    "return_first_non_observer_node",
    # torch.ao.ns.fx.weight_utils
    "extract_weight_from_node",
    "get_conv_fun_weight",
    "get_conv_mod_weight",
    "get_linear_fun_weight",
    "get_linear_mod_weight",
    "get_lstm_mod_weights",
    "get_lstm_weight",
    "get_op_to_type_to_weight_extraction_fn",
    "get_qconv_fun_weight",
    "get_qlinear_fun_weight",
    "get_qlstm_weight",
    "mod_0_weight_detach",
    "mod_weight_bias_0",
    "mod_weight_detach",
    # torch.ao.pruning.sparsifier.utils
    "fqn_to_module",
    "get_arg_info_from_tensor_fqn",
    "module_contains_param",
    "module_to_fqn",
    "swap_module",
    # torch.ao.quantization.backend_config.executorch
    "get_executorch_backend_config",
    # torch.ao.quantization.backend_config.fbgemm
    "get_fbgemm_backend_config",
    # torch.ao.quantization.backend_config.native
    "get_native_backend_config",
    "get_native_backend_config_dict",
    "get_test_only_legacy_native_backend_config",
    "get_test_only_legacy_native_backend_config_dict",
    # torch.ao.quantization.backend_config.onednn
    "get_onednn_backend_config",
    # torch.ao.quantization.backend_config.qnnpack
    "get_qnnpack_backend_config",
    # torch.ao.quantization.backend_config.tensorrt
    "get_tensorrt_backend_config",
    "get_tensorrt_backend_config_dict",
    # torch.ao.quantization.backend_config.utils
    "entry_to_pretty_str",
    "get_fused_module_classes",
    "get_fuser_method_mapping",
    "get_fusion_pattern_to_extra_inputs_getter",
    "get_fusion_pattern_to_root_node_getter",
    "get_module_to_qat_module",
    "get_pattern_to_dtype_configs",
    "get_pattern_to_input_type_to_index",
    "get_qat_module_classes",
    "get_root_module_to_quantized_reference_module",
    "pattern_to_human_readable",
    "remove_boolean_dispatch_from_name",
    # torch.ao.quantization.backend_config.x86
    "get_x86_backend_config",
    # torch.ao.quantization.fuse_modules
    "fuse_known_modules",
    "fuse_modules_qat",
    # torch.ao.quantization.fuser_method_mappings
    "fuse_conv_bn",
    "fuse_conv_bn_relu",
    "fuse_convtranspose_bn",
    "fuse_linear_bn",
    "get_fuser_method",
    "get_fuser_method_new",
    # torch.ao.quantization.fx.convert
    "convert",
    "convert_custom_module",
    "convert_standalone_module",
    "convert_weighted_module",
    # torch.ao.quantization.fx.fuse
    "fuse",
    # torch.ao.quantization.fx.lower_to_fbgemm
    "lower_to_fbgemm",
    # torch.ao.quantization.fx.lower_to_qnnpack
    "lower_to_qnnpack",
    # torch.ao.quantization.fx.pattern_utils
    "get_default_fusion_patterns",
    "get_default_output_activation_post_process_map",
    "get_default_quant_patterns",
    # torch.ao.quantization.fx.prepare
    "insert_observers_for_model",
    "prepare",
    "propagate_dtypes_for_known_nodes",
    # torch.ao.quantization.fx.utils
    "all_node_args_except_first",
    "all_node_args_have_no_tensors",
    "assert_and_get_unique_device",
    "collect_producer_nodes",
    "create_getattr_from_value",
    "create_node_from_old_node_preserve_meta",
    "get_custom_module_class_keys",
    "get_linear_prepack_op_for_dtype",
    "get_new_attr_name_with_prefix",
    "get_non_observable_arg_indexes_and_types",
    "get_qconv_prepack_op",
    "get_skipped_module_name_and_classes",
    "graph_module_from_producer_nodes",
    "maybe_get_next_module",
    "node_arg_is_bias",
    "node_arg_is_weight",
    "return_arg_list",
    # torch.ao.quantization.pt2e.graph_utils
    "find_sequential_partitions",
    "get_equivalent_types",
    "update_equivalent_types_dict",
    # torch.ao.quantization.pt2e.prepare
    "prepare",
    # torch.ao.quantization.pt2e.representation.rewrite
    "reference_representation_rewrite",
    # torch.ao.quantization.pt2e.utils
    "fold_bn_weights_into_conv_node",
    "remove_tensor_overload_for_qdq_ops",
    # torch.ao.quantization.qconfig
    "get_default_qat_qconfig",
    "get_default_qat_qconfig_dict",
    "get_default_qconfig",
    "get_default_qconfig_dict",
    "qconfig_equals",
    # torch.ao.quantization.quantization_mappings
    "get_default_compare_output_module_list",
    "get_default_dynamic_quant_module_mappings",
    "get_default_dynamic_sparse_quant_module_mappings",
    "get_default_float_to_quantized_operator_mappings",
    "get_default_qat_module_mappings",
    "get_default_qconfig_propagation_list",
    "get_default_static_quant_module_mappings",
    "get_default_static_quant_reference_module_mappings",
    "get_default_static_sparse_quant_module_mappings",
    "get_dynamic_quant_module_class",
    "get_embedding_qat_module_mappings",
    "get_embedding_static_quant_module_mappings",
    "get_quantized_operator",
    "get_static_quant_module_class",
    "no_observer_set",
    # torch.ao.quantization.quantize
    "get_default_custom_config_dict",
    # torch.ao.quantization.quantize_fx
    "attach_preserved_attrs_to_model",
    "convert_to_reference_fx",
    # torch.ao.quantization.quantize_jit
    "convert_dynamic_jit",
    "convert_jit",
    "fuse_conv_bn_jit",
    "prepare_dynamic_jit",
    "prepare_jit",
    "quantize_dynamic_jit",
    "quantize_jit",
    "script_qconfig",
    "script_qconfig_dict",
    # torch.ao.quantization.quantize_pt2e
    "convert_pt2e",
    "prepare_pt2e",
    "prepare_qat_pt2e",
    # torch.ao.quantization.quantizer.embedding_quantizer
    "get_embedding_operators_config",
    # torch.ao.quantization.quantizer.xnnpack_quantizer_utils
    "get_bias_qspec",
    "get_input_act_qspec",
    "get_output_act_qspec",
    "get_weight_qspec",
    "propagate_annotation",
    "register_annotator",
    # torch.ao.quantization.utils
    "activation_dtype",
    "activation_is_dynamically_quantized",
    "activation_is_int32_quantized",
    "activation_is_int8_quantized",
    "activation_is_statically_quantized",
    "calculate_qmin_qmax",
    "check_min_max_valid",
    "check_node",
    "determine_qparams",
    "get_combined_dict",
    "get_fqn_to_example_inputs",
    "get_qconfig_dtypes",
    "get_qparam_dict",
    "get_quant_type",
    "get_swapped_custom_module_class",
    "getattr_from_fqn",
    "has_no_children_ignoring_parametrizations",
    "is_per_channel",
    "is_per_tensor",
    "op_is_int8_dynamically_quantized",
    "to_underlying_dtype",
    "validate_qmin_qmax",
    "weight_dtype",
    "weight_is_quantized",
    "weight_is_statically_quantized",
    # torch.backends.cudnn.rnn
    "get_cudnn_mode",
    "init_dropout_state",
    # torch.backends.xeon.run_cpu
    "create_args",
    # torch.cuda.amp.autocast_mode
    "custom_bwd",
    "custom_fwd",
    # torch.cuda.amp.common
    "amp_definitely_not_available",
    # torch.cuda.graphs
    "graph_pool_handle",
    "is_current_stream_capturing",
    "make_graphed_callables",
    # torch.cuda.memory
    "caching_allocator_alloc",
    "caching_allocator_delete",
    "change_current_allocator",
    "empty_cache",
    "get_allocator_backend",
    "list_gpu_processes",
    "max_memory_allocated",
    "max_memory_cached",
    "max_memory_reserved",
    "mem_get_info",
    "memory_allocated",
    "memory_cached",
    "memory_reserved",
    "memory_snapshot",
    "memory_stats",
    "memory_stats_as_nested_dict",
    "memory_summary",
    "reset_accumulated_memory_stats",
    "reset_max_memory_allocated",
    "reset_max_memory_cached",
    "reset_peak_memory_stats",
    "set_per_process_memory_fraction",
    # torch.cuda.nccl
    "all_gather",
    "all_reduce",
    "broadcast",
    "init_rank",
    "reduce",
    "reduce_scatter",
    "unique_id",
    "version",
    # torch.cuda.nvtx
    "range",
    "range_end",
    "range_start",
    # torch.cuda.profiler
    "init",
    "profile",
    "start",
    "stop",
    # torch.cuda.random
    "get_rng_state",
    "get_rng_state_all",
    "initial_seed",
    "manual_seed",
    "manual_seed_all",
    "seed",
    "seed_all",
    "set_rng_state",
    "set_rng_state_all",
    # torch.distributed.algorithms.ddp_comm_hooks.ddp_zero_hook
    "hook_with_zero_step",
    "hook_with_zero_step_interleaved",
    # torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook
    "post_localSGD_hook",
    # torch.distributed.algorithms.ddp_comm_hooks.quantization_hooks
    "quantization_perchannel_hook",
    "quantization_pertensor_hook",
    # torch.distributed.algorithms.model_averaging.utils
    "average_parameters",
    "average_parameters_or_parameter_groups",
    "get_params_to_average",
    # torch.distributed.checkpoint.default_planner
    "create_default_global_load_plan",
    "create_default_global_save_plan",
    "create_default_local_load_plan",
    "create_default_local_save_plan",
    # torch.distributed.checkpoint.optimizer
    "load_sharded_optimizer_state_dict",
    # torch.distributed.checkpoint.planner_helpers
    "create_read_items_for_chunk_list",
    # torch.distributed.checkpoint.state_dict_loader
    "load_state_dict",
    # torch.distributed.checkpoint.state_dict_saver
    "save_state_dict",
    # torch.distributed.checkpoint.utils
    "find_state_dict_object",
    "find_tensor_shard",
    # torch.distributed.collective_utils
    "all_gather",
    "all_gather_object_enforce_type",
    "broadcast",
    # torch.distributed.distributed_c10d
    "all_gather",
    "all_gather_coalesced",
    "all_gather_into_tensor",
    "all_gather_object",
    "all_reduce",
    "all_reduce_coalesced",
    "all_to_all",
    "all_to_all_single",
    "barrier",
    "batch_isend_irecv",
    "broadcast",
    "broadcast_object_list",
    "destroy_process_group",
    "gather",
    "gather_object",
    "get_backend",
    "get_backend_config",
    "get_global_rank",
    "get_group_rank",
    "get_process_group_ranks",
    "get_rank",
    "get_world_size",
    "init_process_group",
    "irecv",
    "is_backend_available",
    "is_gloo_available",
    "is_initialized",
    "is_mpi_available",
    "is_nccl_available",
    "is_torchelastic_launched",
    "is_ucc_available",
    "isend",
    "monitored_barrier",
    "new_group",
    "new_subgroups",
    "new_subgroups_by_enumeration",
    "recv",
    "reduce",
    "reduce_scatter",
    "reduce_scatter_tensor",
    "scatter",
    "scatter_object_list",
    "send",
    "supports_complex",
    # torch.distributed.elastic.events.handlers
    "get_logging_handler",
    # torch.distributed.elastic.metrics.api
    "configure",
    "getStream",
    "get_elapsed_time_ms",
    "prof",
    "profile",
    "publish_metric",
    "put_metric",
    # torch.distributed.elastic.multiprocessing.api
    "get_std_cm",
    "to_map",
    # torch.distributed.elastic.multiprocessing.errors.handlers
    "get_error_handler",
    # torch.distributed.elastic.multiprocessing.redirects
    "get_libc",
    "redirect",
    # torch.distributed.elastic.multiprocessing.tail_log
    "tail_logfile",
    # torch.distributed.elastic.rendezvous.dynamic_rendezvous
    "get_method_name",
    # torch.distributed.elastic.rendezvous.etcd_rendezvous
    "create_rdzv_handler",
    # torch.distributed.elastic.rendezvous.etcd_server
    "find_free_port",
    "stop_etcd",
    # torch.distributed.elastic.rendezvous.etcd_store
    "cas_delay",
    # torch.distributed.elastic.rendezvous.static_tcp_rendezvous
    "create_rdzv_handler",
    # torch.distributed.elastic.rendezvous.utils
    "parse_rendezvous_endpoint",
    # torch.distributed.elastic.timer.api
    "configure",
    "expires",
    # torch.distributed.elastic.utils.api
    "get_env_variable_or_raise",
    "get_socket_with_port",
    # torch.distributed.elastic.utils.distributed
    "create_c10d_store",
    "get_free_port",
    "get_socket_with_port",
    # torch.distributed.elastic.utils.log_level
    "get_log_level",
    # torch.distributed.elastic.utils.logging
    "get_logger",
    # torch.distributed.elastic.utils.store
    "barrier",
    "get_all",
    "synchronize",
    "store_timeout",
    # torch.distributed.fsdp.wrap
    "always_wrap_policy",
    "enable_wrap",
    "lambda_auto_wrap_policy",
    "size_based_auto_wrap_policy",
    "transformer_auto_wrap_policy",
    "wrap",
    # torch.distributed.nn.functional
    "all_gather",
    "all_reduce",
    "all_to_all",
    "all_to_all_single",
    "broadcast",
    "gather",
    "reduce",
    "reduce_scatter",
    "scatter",
    # torch.distributed.nn.jit.instantiator
    "get_arg_return_types_from_interface",
    "instantiate_non_scriptable_remote_module_template",
    "instantiate_scriptable_remote_module_template",
    # torch.distributed.nn.jit.templates.remote_module_template
    "get_remote_module_template",
    # torch.distributed.optim.utils
    "as_functional_optim",
    "register_functional_optim",
    # torch.distributed.rendezvous
    "register_rendezvous_handler",
    "rendezvous",
    # torch.distributed.rpc.api
    "get_worker_info",
    "method_factory",
    "new_method",
    "remote",
    "rpc_async",
    "rpc_sync",
    "shutdown",
    # torch.distributed.rpc.backend_registry
    "backend_registered",
    "construct_rpc_backend_options",
    "init_backend",
    "register_backend",
    # torch.distributed.rpc.internal
    "deserialize",
    "serialize",
    # torch.distributed.tensor.parallel.api
    "parallelize_module",
    # torch.distributed.tensor.parallel.input_reshard
    "input_reshard",
    # torch.distributed.tensor.parallel.loss
    "loss_parallel",
    # torch.distributed.tensor.parallel.style
    "make_sharded_output_tensor",
    # torch.distributions.utils
    "broadcast_all",
    "clamp_probs",
    "logits_to_probs",
    "probs_to_logits",
    "tril_matrix_to_vec",
    "vec_to_tril_matrix",
    # torch.functional
    "align_tensors",
    "atleast_1d",
    "atleast_2d",
    "atleast_3d",
    "block_diag",
    "broadcast_shapes",
    "broadcast_tensors",
    "cartesian_prod",
    "cdist",
    "chain_matmul",
    "einsum",
    "lu",
    "meshgrid",
    "norm",
    "split",
    "stft",
    "tensordot",
    "unique",
    "unique_consecutive",
    "unravel_index",
    # torch.fx.annotate
    "annotate",
    # torch.fx.experimental.accelerator_partitioner
    "check_dependency",
    "combine_two_partitions",
    "get_bfs_level_partition",
    "get_device_partition_stats",
    "get_device_to_partitions_mapping",
    "get_logical_id_to_device",
    "get_node_to_partition_mapping",
    "reorganize_partitions",
    "reset_partition_device",
    "set_parents_and_children",
    # torch.fx.experimental.const_fold
    "get_unique_attr_name_in_module",
    "split_const_subgraphs",
    # torch.fx.experimental.debug
    "set_trace",
    # torch.fx.experimental.graph_gradual_typechecker
    "adaptiveavgpool2d_check",
    "adaptiveavgpool2d_inference_rule",
    "add_inference_rule",
    "all_eq",
    "bn2d_inference_rule",
    "broadcast_types",
    "calculate_out_dimension",
    "conv2d_inference_rule",
    "conv_refinement_rule",
    "conv_rule",
    "element_wise_eq",
    "expand_to_tensor_dim",
    "first_two_eq",
    "flatten_check",
    "flatten_inference_rule",
    "flatten_refinement_rule",
    "get_attr_inference_rule",
    "get_greatest_upper_bound",
    "get_parameter",
    "linear_check",
    "linear_inference_rule",
    "linear_refinement_rule",
    "maxpool2d_check",
    "maxpool2d_inference_rule",
    "register_algebraic_expressions_inference_rule",
    "register_inference_rule",
    "register_refinement_rule",
    "relu_inference_rule",
    "reshape_inference_rule",
    "transpose_inference_rule",
    # torch.fx.experimental.merge_matmul
    "are_nodes_independent",
    "may_depend_on",
    "merge_matmul",
    "split_result_tensors",
    # torch.fx.experimental.meta_tracer
    "embedding_override",
    "functional_relu_override",
    "gen_constructor_wrapper",
    "nn_layernorm_override",
    "proxys_to_metas",
    "symbolic_trace",
    "torch_abs_override",
    "torch_nn_relu_override",
    "torch_relu_override",
    "torch_where_override",
    # torch.fx.experimental.migrate_gradual_types.constraint
    "is_algebraic_expression",
    "is_bool_expr",
    "is_dim",
    # torch.fx.experimental.migrate_gradual_types.constraint_generator
    "adaptive_inference_rule",
    "add_layer_norm_constraints",
    "add_linear_constraints",
    "arange_inference_rule",
    "assert_inference_rule",
    "batchnorm_inference_rule",
    "bmm_inference_rule",
    "broadcasting_inference_rule",
    "conv2d_inference_rule",
    "cumsum_inference_rule",
    "embedding_inference_rule",
    "embedding_inference_rule_functional",
    "eq_inference_rule",
    "equality_inference_rule",
    "expand_inference_rule",
    "flatten_inference_rule",
    "full_inference_rule",
    "gen_broadcasting_constraints",
    "gen_embedding_rules",
    "gen_layer_norm_constraints",
    "generate_flatten_constraints",
    "get_attr_inference_rule",
    "getitem_inference_rule",
    "gt_inference_rule",
    "index_select_inference_rule",
    "layer_norm_functional",
    "layer_norm_inference_rule",
    "linear_constraints",
    "linear_inference_rule",
    "lt_inference_rule",
    "masked_fill_inference_rule",
    "maxpool_inference_rule",
    "neq_inference_rule",
    "range_check",
    "register_inference_rule",
    "relu_inference_rule",
    "reshape_inference_rule",
    "size_inference_rule",
    "tensor_inference_rule",
    "torch_dim_inference_rule",
    "torch_linear_inference_rule",
    "transpose_inference_rule",
    "type_inference_rule",
    "view_inference_rule",
    # torch.fx.experimental.migrate_gradual_types.constraint_transformation
    "apply_padding",
    "broadcast_dim",
    "calc_last_two_dims",
    "create_equality_constraints_for_broadcasting",
    "gen_all_reshape_possibilities",
    "gen_broadcasting_constraints",
    "gen_consistency_constraints",
    "gen_greatest_upper_bound",
    "gen_lists_of_dims",
    "generate_all_broadcasting_possibilities_no_padding",
    "generate_all_int_dyn_dim_possibilities",
    "generate_binconstraint_d",
    "generate_binconstraint_t",
    "generate_broadcasting",
    "generate_calc_conv",
    "generate_calc_maxpool",
    "generate_calc_product",
    "generate_conj",
    "generate_d_gub",
    "generate_disj",
    "generate_gub",
    "generate_reshape",
    "is_dim_div_by_target",
    "is_target_div_by_dim",
    "no_broadcast_dim_with_index",
    "register_transformation_rule",
    "transform_constraint",
    "transform_get_item",
    "transform_get_item_tensor",
    "transform_index_select",
    "transform_transpose",
    "valid_index",
    "valid_index_tensor",
    # torch.fx.experimental.migrate_gradual_types.transform_to_z3
    "evaluate_conditional_with_constraints",
    # torch.fx.experimental.migrate_gradual_types.util
    "gen_bvar",
    "gen_dvar",
    "gen_nat_constraints",
    "gen_tensor_dims",
    "gen_tvar",
    # torch.fx.experimental.optimization
    "extract_subgraph",
    "fuse",
    "gen_mkl_autotuner",
    "matches_module_pattern",
    "modules_to_mkldnn",
    "optimize_for_inference",
    "remove_dropout",
    "replace_node_module",
    "reset_modules",
    "use_mkl_length",
    # torch.fx.experimental.partitioner_utils
    "get_comm_latency_between",
    "get_extra_size_of",
    "get_latency_of_one_partition",
    "get_latency_of_partitioned_graph",
    "get_partition_to_latency_mapping",
    # torch.fx.experimental.proxy_tensor
    "decompose",
    "disable_autocast_cache",
    "disable_proxy_modes_tracing",
    "dispatch_trace",
    "extract_val",
    "fake_signature",
    "fetch_sym_proxy",
    "fetch_object_proxy",
    "get_innermost_proxy_mode",
    "get_isolated_graphmodule",
    "get_proxy_slot",
    "get_torch_dispatch_modes",
    "has_proxy_slot",
    "is_sym_node",
    "maybe_handle_decomp",
    "proxy_call",
    "set_meta",
    "set_original_aten_op",
    "set_proxy_slot",
    "snapshot_fake",
    "thunkify",
    "track_tensor",
    "track_tensor_tree",
    "wrap_key",
    "wrapper_and_args_for_make_fx",
    # torch.fx.experimental.recording
    "record_shapeenv_event",
    "replay_shape_env_events",
    "shape_env_check_state_equal",
    # torch.fx.experimental.sym_node
    "ceil_impl",
    "floor_ceil_helper",
    "floor_impl",
    "method_to_operator",
    "sympy_is_channels_last_contiguous_2d",
    "sympy_is_channels_last_contiguous_3d",
    "sympy_is_channels_last_strides_2d",
    "sympy_is_channels_last_strides_3d",
    "sympy_is_channels_last_strides_generic",
    "sympy_is_contiguous",
    "sympy_is_contiguous_generic",
    "to_node",
    "wrap_node",
    "sym_sqrt",
    "sym_ite",
    # torch.fx.experimental.symbolic_shapes
    "bind_symbols",
    "cast_symbool_to_symint_guardless",
    "create_contiguous",
    "error",
    "eval_guards",
    "eval_is_non_overlapping_and_dense",
    "expect_true",
    "find_symbol_binding_fx_nodes",
    "free_symbols",
    "free_unbacked_symbols",
    "fx_placeholder_targets",
    "fx_placeholder_vals",
    "guard_bool",
    "guard_float",
    "guard_int",
    "guard_scalar",
    "has_hint",
    "has_symbolic_sizes_strides",
    "is_channels_last_contiguous_2d",
    "is_channels_last_contiguous_3d",
    "is_channels_last_strides_2d",
    "is_channels_last_strides_3d",
    "is_contiguous",
    "is_non_overlapping_and_dense_indicator",
    "is_nested_int",
    "is_symbol_binding_fx_node",
    "is_symbolic",
    # torch.fx.experimental.unification.core
    "reify",
    # torch.fx.experimental.unification.match
    "edge",
    "match",
    "ordering",
    "supercedes",
    # torch.fx.experimental.unification.more
    "reify_object",
    "unifiable",
    "unify_object",
    # torch.fx.experimental.unification.multipledispatch.conflict
    "ambiguities",
    "ambiguous",
    "consistent",
    "edge",
    "ordering",
    "super_signature",
    "supercedes",
    # torch.fx.experimental.unification.multipledispatch.core
    "dispatch",
    "ismethod",
    # torch.fx.experimental.unification.multipledispatch.dispatcher
    "ambiguity_warn",
    "halt_ordering",
    "restart_ordering",
    "source",
    "str_signature",
    "variadic_signature_matches",
    "variadic_signature_matches_iter",
    "warning_text",
    # torch.fx.experimental.unification.multipledispatch.utils
    "expand_tuples",
    "groupby",
    "raises",
    "reverse_dict",
    # torch.fx.experimental.unification.multipledispatch.variadic
    "isvariadic",
    # torch.fx.experimental.unification.unification_tools
    "assoc",
    "assoc_in",
    "dissoc",
    "first",
    "get_in",
    "getter",
    "groupby",
    "itemfilter",
    "itemmap",
    "keyfilter",
    "keymap",
    "merge",
    "merge_with",
    "update_in",
    "valfilter",
    "valmap",
    # torch.fx.experimental.unification.utils
    "freeze",
    "hashable",
    "raises",
    "reverse_dict",
    "transitive_get",
    "xfail",
    # torch.fx.experimental.unification.variable
    "var",
    "vars",
    # torch.fx.experimental.unify_refinements
    "check_for_type_equality",
    "convert_eq",
    "infer_symbolic_types",
    "infer_symbolic_types_single_pass",
    "substitute_all_types",
    "substitute_solution_one_type",
    "unify_eq",
    # torch.fx.experimental.validator
    "bisect",
    "translation_validation_enabled",
    "translation_validation_timeout",
    "z3op",
    "z3str",
    # torch.fx.graph_module
    "reduce_deploy_graph_module",
    "reduce_graph_module",
    "reduce_package_graph_module",
    # torch.fx.node
    "has_side_effect",
    "map_aggregate",
    "map_arg",
    # torch.fx.operator_schemas
    "check_for_mutable_operation",
    "create_type_hint",
    "get_signature_for_torch_op",
    "normalize_function",
    "normalize_module",
    "type_matches",
    # torch.fx.passes.annotate_getitem_nodes
    "annotate_getitem_nodes",
    # torch.fx.passes.backends.cudagraphs
    "partition_cudagraphs",
    # torch.fx.passes.dialect.common.cse_pass
    "get_CSE_banned_ops",
    # torch.fx.passes.graph_manipulation
    "get_size_of_all_nodes",
    "get_size_of_node",
    "get_tensor_meta",
    "replace_target_nodes_with",
    # torch.fx.passes.infra.pass_manager
    "pass_result_wrapper",
    "this_before_that_pass_constraint",
    # torch.fx.passes.operator_support
    "any_chain",
    "chain",
    "create_op_support",
    # torch.fx.passes.param_fetch
    "default_matching",
    "extract_attrs_for_lowering",
    "lift_lowering_attrs_to_nodes",
    # torch.fx.passes.pass_manager
    "inplace_wrapper",
    "log_hook",
    "loop_pass",
    "these_before_those_pass_constraint",
    "this_before_that_pass_constraint",
    # torch.fx.passes.reinplace
    "reinplace",
    # torch.fx.passes.split_module
    "split_module",
    # torch.fx.passes.split_utils
    "getattr_recursive",
    "setattr_recursive",
    "split_by_tags",
    # torch.fx.passes.splitter_base
    "generate_inputs_for_submodules",
    # torch.fx.passes.tools_common
    "get_acc_ops_name",
    "get_node_target",
    "is_node_output_tensor",
    "legalize_graph",
    # torch.fx.passes.utils.common
    "compare_graphs",
    "lift_subgraph_as_module",
    # torch.fx.passes.utils.fuser_utils
    "erase_nodes",
    "fuse_as_graphmodule",
    "fuse_by_partitions",
    "insert_subgm",
    "topo_sort",
    "validate_partition",
    # torch.fx.passes.utils.source_matcher_utils
    "check_subgraphs_connected",
    "get_source_partitions",
    # torch.fx.proxy
    "assert_fn",
    # torch.fx.subgraph_rewriter
    "replace_pattern",
    "replace_pattern_with_filters",
    # torch.fx.tensor_type
    "is_consistent",
    "is_more_precise",
    # torch.fx.traceback
    "format_stack",
    "get_current_meta",
    "has_preserved_node_meta",
    "preserve_node_meta",
    "reset_grad_fn_seq_nr",
    "set_current_meta",
    "set_grad_fn_seq_nr",
    "set_stack_trace",
    # torch.jit.annotations
    "ann_to_type",
    "check_fn",
    "get_enum_value_type",
    "get_param_names",
    "get_signature",
    "get_type_line",
    "is_function_or_method",
    "is_tensor",
    "is_vararg",
    "parse_type_line",
    "split_type_line",
    "try_ann_to_type",
    "try_real_annotations",
    # torch.jit.frontend
    "build_class_def",
    "build_def",
    "build_ignore_context_manager",
    "build_param",
    "build_param_list",
    "build_stmts",
    "build_withitems",
    "find_before",
    "get_class_assigns",
    "get_class_properties",
    "get_default_args",
    "get_default_args_for_class",
    "get_jit_class_def",
    "get_jit_def",
    "is_reserved_name",
    "is_torch_jit_ignore_context_manager",
    # torch.jit.generate_bytecode
    "format_bytecode",
    "generate_upgraders_bytecode",
    # torch.jit.quantized
    "apply_permutation",
    "quantize_linear_modules",
    "quantize_rnn_cell_modules",
    "quantize_rnn_modules",
    # torch.library
    "define",
    "get_ctx",
    "impl",
    "impl_abstract",
    # torch.masked.maskedtensor.core
    "is_masked_tensor",
    # torch.masked.maskedtensor.creation
    "as_masked_tensor",
    "masked_tensor",
    # torch.multiprocessing.pool
    "clean_worker",
    # torch.multiprocessing.reductions
    "fd_id",
    "init_reductions",
    "rebuild_cuda_tensor",
    "rebuild_meta_tensor",
    "rebuild_event",
    "rebuild_nested_tensor",
    "rebuild_sparse_coo_tensor",
    "rebuild_sparse_compressed_tensor",
    "rebuild_storage_empty",
    "rebuild_storage_fd",
    "rebuild_storage_filename",
    "rebuild_tensor",
    "rebuild_typed_storage",
    "rebuild_typed_storage_child",
    "reduce_event",
    "reduce_storage",
    "reduce_tensor",
    "reduce_typed_storage",
    "reduce_typed_storage_child",
    "storage_from_cache",
    # torch.multiprocessing.spawn
    "start_processes",
    # torch.nn.functional
    "adaptive_max_pool1d_with_indices",
    "adaptive_max_pool2d_with_indices",
    "adaptive_max_pool3d_with_indices",
    "assert_int_or_pair",
    "fractional_max_pool2d_with_indices",
    "fractional_max_pool3d_with_indices",
    "max_pool1d_with_indices",
    "max_pool2d_with_indices",
    "max_pool3d_with_indices",
    "multi_head_attention_forward",
    # torch.nn.grad
    "conv1d_input",
    "conv1d_weight",
    "conv2d_input",
    "conv2d_weight",
    "conv3d_input",
    "conv3d_weight",
    # torch.nn.init
    "constant",
    "dirac",
    "eye",
    "kaiming_normal",
    "kaiming_uniform",
    "normal",
    "orthogonal",
    "sparse",
    "uniform",
    "xavier_normal",
    "xavier_uniform",
    # torch.nn.modules.rnn
    "apply_permutation",
    # torch.nn.modules.utils
    "consume_prefix_in_state_dict_if_present",
    # torch.nn.parallel.comm
    "broadcast",
    "broadcast_coalesced",
    "gather",
    "reduce_add",
    "reduce_add_coalesced",
    "scatter",
    # torch.nn.parallel.data_parallel
    "data_parallel",
    # torch.nn.parallel.parallel_apply
    "get_a_var",
    "parallel_apply",
    # torch.nn.parallel.replicate
    "replicate",
    # torch.nn.parallel.scatter_gather
    "gather",
    "is_namedtuple",
    "scatter",
    "scatter_kwargs",
    # torch.nn.parameter
    "is_lazy",
    # torch.nn.utils.clip_grad
    "clip_grad_norm",
    "clip_grad_norm_",
    "clip_grad_value_",
    # torch.nn.utils.convert_parameters
    "parameters_to_vector",
    "vector_to_parameters",
    # torch.nn.utils.fusion
    "fuse_conv_bn_eval",
    "fuse_conv_bn_weights",
    "fuse_linear_bn_eval",
    "fuse_linear_bn_weights",
    # torch.nn.utils.init
    "skip_init",
    # torch.nn.utils.memory_format
    "convert_conv2d_weight_memory_format",
    # torch.nn.utils.parametrizations
    "weight_norm",
    # torch.nn.utils.parametrize
    "transfer_parametrizations_and_params",
    "type_before_parametrizations",
    # torch.nn.utils.rnn
    "bind",
    "invert_permutation",
    # torch.nn.utils.spectral_norm
    "remove_spectral_norm",
    "spectral_norm",
    # torch.nn.utils.weight_norm
    "remove_weight_norm",
    "weight_norm",
    # torch.onnx.operators
    "reshape_from_tensor_shape",
    "shape_as_tensor",
    # torch.onnx.symbolic_caffe2
    "add",
    "avg_pool2d",
    "cat",
    "conv2d",
    "conv2d_relu",
    "conv_prepack",
    "dequantize",
    "linear",
    "linear_prepack",
    "max_pool2d",
    "nchw2nhwc",
    "nhwc2nchw",
    "quantize_per_tensor",
    "register_quantized_ops",
    "relu",
    "reshape",
    "sigmoid",
    "slice",
    "upsample_nearest2d",
    # torch.onnx.symbolic_helper
    "args_have_same_dtype",
    "check_training_mode",
    "dequantize_helper",
    "is_complex_value",
    "quantize_helper",
    "quantized_args",
    "requantize_bias_helper",
    # torch.onnx.symbolic_opset10
    "dequantize",
    "div",
    "embedding_bag",
    "fake_quantize_per_tensor_affine",
    "flip",
    "fmod",
    "isfinite",
    "isinf",
    "nan_to_num",
    "quantize_per_tensor",
    "quantized_add",
    "quantized_add_relu",
    "quantized_cat",
    "quantized_conv1d",
    "quantized_conv1d_relu",
    "quantized_conv2d",
    "quantized_conv2d_relu",
    "quantized_conv3d",
    "quantized_conv3d_relu",
    "quantized_conv_transpose1d",
    "quantized_conv_transpose2d",
    "quantized_conv_transpose3d",
    "quantized_group_norm",
    "quantized_hardswish",
    "quantized_instance_norm",
    "quantized_layer_norm",
    "quantized_leaky_relu",
    "quantized_linear",
    "quantized_linear_relu",
    "quantized_mul",
    "quantized_sigmoid",
    "slice",
    "sort",
    "topk",
    # torch.onnx.symbolic_opset11
    "Delete",
    "add",
    "append",
    "arange",
    "argsort",
    "atleast_1d",
    "atleast_2d",
    "atleast_3d",
    "cat",
    "chunk",
    "clamp",
    "clamp_max",
    "clamp_min",
    "constant_pad_nd",
    "cumsum",
    "embedding_bag",
    "embedding_renorm",
    "flatten",
    "gather",
    "hardtanh",
    "hstack",
    "im2col",
    "index",
    "index_copy",
    "index_fill",
    "index_put",
    "insert",
    "linalg_det",
    "linalg_vector_norm",
    "logdet",
    "masked_scatter",
    "masked_select",
    "mm",
    "narrow",
    "normal",
    "pad",
    "pixel_shuffle",
    "pop",
    "prim_constant_chunk",
    "reflection_pad",
    "relu6",
    "remainder",
    "replication_pad",
    "round",
    "scatter",
    "select",
    "size",
    "sort",
    "split",
    "split_with_sizes",
    "squeeze",
    "stack",
    "topk",
    "unbind",
    "unique_dim",
    "unsqueeze",
    "vstack",
    # torch.onnx.symbolic_opset12
    "argmax",
    "argmin",
    "binary_cross_entropy_with_logits",
    "celu",
    "cross_entropy_loss",
    "dropout",
    "einsum",
    "ge",
    "le",
    "native_dropout",
    "nll_loss",
    "nll_loss2d",
    "nll_loss_nd",
    "outer",
    "pow",
    "tensordot",
    "unfold",
    # torch.onnx.symbolic_opset13
    "diagonal",
    "fake_quantize_per_channel_affine",
    "fake_quantize_per_tensor_affine",
    "frobenius_norm",
    "log_softmax",
    "nonzero_numpy",
    "quantized_conv1d",
    "quantized_conv1d_relu",
    "quantized_conv2d",
    "quantized_conv2d_relu",
    "quantized_conv3d",
    "quantized_conv3d_relu",
    "quantized_conv_transpose1d",
    "quantized_conv_transpose2d",
    "quantized_conv_transpose3d",
    "quantized_linear",
    "quantized_linear_relu",
    "repeat_interleave",
    "softmax",
    "split",
    "split_with_sizes",
    "tensor_split",
    "tile",
    "unbind",
    "unflatten",
    "unsafe_chunk",
    "unsafe_split",
    "unsafe_split_with_sizes",
    "where",
    # torch.onnx.symbolic_opset14
    "batch_norm",
    "hardswish",
    "quantized_hardswish",
    "reshape",
    "scaled_dot_product_attention",
    "tril",
    "triu",
    # torch.onnx.symbolic_opset15
    "aten__is_",
    "aten__isnot_",
    "bernoulli",
    "prim_unchecked_cast",
    # torch.onnx.symbolic_opset16
    "grid_sampler",
    "scatter_add",
    "scatter_reduce",
    # torch.onnx.symbolic_opset17
    "layer_norm",
    "stft",
    # torch.onnx.symbolic_opset18
    "col2im",
    # torch.onnx.symbolic_opset7
    "max",
    "min",
    # torch.onnx.symbolic_opset8
    "addmm",
    "bmm",
    "empty",
    "empty_like",
    "flatten",
    "full",
    "full_like",
    "gt",
    "lt",
    "matmul",
    "mm",
    "ones",
    "ones_like",
    "prelu",
    "repeat",
    "zeros",
    "zeros_like",
    # torch.onnx.symbolic_opset9
    "abs",
    "acos",
    "adaptive_avg_pool1d",
    "adaptive_avg_pool2d",
    "adaptive_avg_pool3d",
    "adaptive_max_pool1d",
    "adaptive_max_pool2d",
    "adaptive_max_pool3d",
    "add",
    "addcmul",
    "addmm",
    "alias",
    "amax",
    "amin",
    "aminmax",
    "arange",
    "argmax",
    "argmin",
    "as_strided",
    "as_tensor",
    "asin",
    "atan",
    "atan2",
    "avg_pool1d",
    "avg_pool2d",
    "avg_pool3d",
    "baddbmm",
    "batch_norm",
    "bernoulli",
    "bitwise_not",
    "bitwise_or",
    "bmm",
    "broadcast_tensors",
    "broadcast_to",
    "bucketize",
    "cat",
    "cdist",
    "ceil",
    "clamp",
    "clamp_max",
    "clamp_min",
    "clone",
    "constant_pad_nd",
    "contiguous",
    "conv1d",
    "conv2d",
    "conv3d",
    "conv_tbc",
    "conv_transpose1d",
    "conv_transpose2d",
    "conv_transpose3d",
    "convert_element_type",
    "convolution",
    "cos",
    "cosine_similarity",
    "cross",
    "cumsum",
    "detach",
    "dim",
    "div",
    "dot",
    "dropout",
    "elu",
    "embedding",
    "embedding_bag",
    "empty",
    "empty_like",
    "eq",
    "erf",
    "exp",
    "expand",
    "expand_as",
    "eye",
    "fill",
    "flatten",
    "floor",
    "floor_divide",
    "floordiv",
    "frobenius_norm",
    "full",
    "full_like",
    "gather",
    "ge",
    "gelu",
    "get_pool_ceil_padding",
    "glu",
    "group_norm",
    "gru",
    "gt",
    "hann_window",
    "hardshrink",
    "hardsigmoid",
    "hardswish",
    "hardtanh",
    "index",
    "index_add",
    "index_copy",
    "index_fill",
    "index_put",
    "index_select",
    "instance_norm",
    "is_floating_point",
    "is_pinned",
    "isnan",
    "item",
    "kl_div",
    "layer_norm",
    "le",
    "leaky_relu",
    "lerp",
    "lift",
    "linalg_cross",
    "linalg_matrix_norm",
    "linalg_norm",
    "linalg_vector_norm",
    "linear",
    "linspace",
    "log",
    "log10",
    "log1p",
    "log2",
    "log_sigmoid",
    "log_softmax",
    "logical_and",
    "logical_not",
    "logical_or",
    "logical_xor",
    "logit",
    "logsumexp",
    "lstm",
    "lstm_cell",
    "lt",
    "masked_fill",
    "masked_fill_",
    "matmul",
    "max",
    "max_pool1d",
    "max_pool1d_with_indices",
    "max_pool2d",
    "max_pool2d_with_indices",
    "max_pool3d",
    "max_pool3d_with_indices",
    "maximum",
    "meshgrid",
    "min",
    "minimum",
    "mish",
    "mm",
    "movedim",
    "mse_loss",
    "mul",
    "multinomial",
    "mv",
    "narrow",
    "native_layer_norm",
    "ne",
    "neg",
    "new_empty",
    "new_full",
    "new_ones",
    "new_zeros",
    "nonzero",
    "nonzero_numpy",
    "noop_complex_operators",
    "norm",
    "numel",
    "numpy_T",
    "one_hot",
    "ones",
    "ones_like",
    "onnx_placeholder",
    "overload_by_arg_count",
    "pad",
    "pairwise_distance",
    "permute",
    "pixel_shuffle",
    "pixel_unshuffle",
    "pow",
    "prelu",
    "prim_constant",
    "prim_constant_chunk",
    "prim_constant_split",
    "prim_data",
    "prim_device",
    "prim_dtype",
    "prim_if",
    "prim_layout",
    "prim_list_construct",
    "prim_list_unpack",
    "prim_loop",
    "prim_max",
    "prim_min",
    "prim_shape",
    "prim_tolist",
    "prim_tuple_construct",
    "prim_type",
    "prim_unchecked_cast",
    "prim_uninitialized",
    "rand",
    "rand_like",
    "randint",
    "randint_like",
    "randn",
    "randn_like",
    "reciprocal",
    "reflection_pad",
    "relu",
    "relu6",
    "remainder",
    "repeat",
    "repeat_interleave",
    "replication_pad",
    "reshape",
    "reshape_as",
    "rnn_relu",
    "rnn_tanh",
    "roll",
    "rrelu",
    "rsqrt",
    "rsub",
    "scalar_tensor",
    "scatter",
    "scatter_add",
    "select",
    "selu",
    "sigmoid",
    "sign",
    "silu",
    "sin",
    "size",
    "slice",
    "softmax",
    "softplus",
    "softshrink",
    "sort",
    "split",
    "split_with_sizes",
    "sqrt",
    "square",
    "squeeze",
    "stack",
    "std",
    "std_mean",
    "sub",
    "t",
    "take",
    "tan",
    "tanh",
    "tanhshrink",
    "tensor",
    "threshold",
    "to",
    "topk",
    "transpose",
    "true_divide",
    "type_as",
    "unbind",
    "unfold",
    "unsafe_chunk",
    "unsafe_split",
    "unsafe_split_with_sizes",
    "unsqueeze",
    "unsupported_complex_operators",
    "unused",
    "upsample_bilinear2d",
    "upsample_linear1d",
    "upsample_nearest1d",
    "upsample_nearest2d",
    "upsample_nearest3d",
    "upsample_trilinear3d",
    "var",
    "var_mean",
    "view",
    "view_as",
    "where",
    "wrap_logical_op_with_cast_to",
    "wrap_logical_op_with_negation",
    "zero",
    "zeros",
    "zeros_like",
    # torch.onnx.utils
    "disable_apex_o2_state_dict_hook",
    "export",
    "export_to_pretty_string",
    "exporter_context",
    "is_in_onnx_export",
    "model_signature",
    "register_custom_op_symbolic",
    "select_model_mode_for_export",
    "setup_onnx_logging",
    "unconvertible_ops",
    "unpack_quantized_tensor",
    "warn_on_static_input_change",
    # torch.onnx.verification
    "check_export_model_diff",
    "verify",
    "verify_aten_graph",
    # torch.optim.adadelta
    "adadelta",
    # torch.optim.adagrad
    "adagrad",
    # torch.optim.adam
    "adam",
    # torch.optim.adamax
    "adamax",
    # torch.optim.adamw
    "adamw",
    # torch.optim.asgd
    "asgd",
    # torch.optim.nadam
    "nadam",
    # torch.optim.optimizer
    "register_optimizer_step_post_hook",
    "register_optimizer_step_pre_hook",
    # torch.optim.radam
    "radam",
    # torch.optim.rmsprop
    "rmsprop",
    # torch.optim.rprop
    "rprop",
    # torch.optim.sgd
    "sgd",
    # torch.optim.swa_utils
    "get_ema_avg_fn",
    "get_ema_multi_avg_fn",
    "get_swa_avg_fn",
    "get_swa_multi_avg_fn",
    "update_bn",
    # torch.overrides
    "enable_reentrant_dispatch",
    # torch.package.analyze.find_first_use_of_broken_modules
    "find_first_use_of_broken_modules",
    # torch.package.analyze.is_from_package
    "is_from_package",
    # torch.package.analyze.trace_dependencies
    "trace_dependencies",
    # torch.profiler.itt
    "range",
    # torch.profiler.profiler
    "schedule",
    "supported_activities",
    "tensorboard_trace_handler",
    # torch.return_types
    "pytree_register_structseq",
    # torch.serialization
    "check_module_version_greater_or_equal",
    "default_restore_location",
    "load",
    "location_tag",
    "mkdtemp",
    "normalize_storage_type",
    "save",
    "storage_to_tensor_type",
    "validate_cuda_device",
    "validate_hpu_device",
    # torch.signal.windows.windows
    "bartlett",
    "blackman",
    "cosine",
    "exponential",
    "gaussian",
    "general_cosine",
    "general_hamming",
    "hamming",
    "hann",
    "kaiser",
    "nuttall",
    # torch.sparse.semi_structured
    "to_sparse_semi_structured",
    # torch.utils.backend_registration
    "generate_methods_for_privateuse1_backend",
    "rename_privateuse1_backend",
    # torch.utils.benchmark.examples.blas_compare_setup
    "conda_run",
    # torch.utils.benchmark.examples.op_benchmark
    "assert_dicts_equal",
    # torch.utils.benchmark.op_fuzzers.spectral
    "power_range",
    # torch.utils.benchmark.utils.common
    "ordered_unique",
    "select_unit",
    "set_torch_threads",
    "trim_sigfig",
    "unit_to_english",
    # torch.utils.benchmark.utils.compare
    "optional_min",
    # torch.utils.benchmark.utils.compile
    "bench_all",
    "bench_loop",
    "benchmark_compile",
    # torch.utils.benchmark.utils.cpp_jit
    "compile_callgrind_template",
    "compile_timeit_template",
    "get_compat_bindings",
    # torch.utils.benchmark.utils.fuzzer
    "dtype_size",
    "prod",
    # torch.utils.benchmark.utils.timer
    "timer",
    # torch.utils.benchmark.utils.valgrind_wrapper.timer_interface
    "wrapper_singleton",
    # torch.utils.bundled_inputs
    "augment_many_model_functions_with_bundled_inputs",
    "augment_model_with_bundled_inputs",
    "bundle_inputs",
    "bundle_large_tensor",
    "bundle_randn",
    # torch.utils.checkpoint
    "check_backward_validity",
    "detach_variable",
    "get_device_states",
    "noop_context_fn",
    "set_checkpoint_early_stop",
    "set_device_states",
    # torch.utils.collect_env
    "check_release_file",
    "get_cachingallocator_config",
    "get_clang_version",
    "get_cmake_version",
    "get_conda_packages",
    "get_cpu_info",
    "get_cuda_module_loading_config",
    "get_cudnn_version",
    "get_env_info",
    "get_gcc_version",
    "get_gpu_info",
    "get_libc_version",
    "get_lsb_version",
    "get_mac_version",
    "get_nvidia_driver_version",
    "get_nvidia_smi",
    "get_os",
    "get_pip_packages",
    "get_platform",
    "get_pretty_env_info",
    "get_python_platform",
    "get_running_cuda_version",
    "get_windows_version",
    "is_xnnpack_available",
    "pretty_str",
    # torch.utils.cpp_backtrace
    "get_cpp_backtrace",
    # torch.utils.cpp_extension
    "check_compiler_is_gcc",
    "check_compiler_ok_for_platform",
    "get_cxx_compiler",
    "get_default_build_root",
    "library_paths",
    "remove_extension_h_precompiler_headers",
    # torch.utils.data.backward_compatibility
    "worker_init_fn",
    # torch.utils.data.datapipes.dataframe.dataframe_wrapper
    "concat",
    "create_dataframe",
    "get_columns",
    "get_df_wrapper",
    "get_item",
    "get_len",
    "is_column",
    "is_dataframe",
    "iterate",
    "set_df_wrapper",
    # torch.utils.data.datapipes.dataframe.dataframes
    "disable_capture",
    "get_val",
    # torch.utils.data.datapipes.gen_pyi
    "extract_class_name",
    "extract_method_name",
    "find_file_paths",
    "gen_from_template",
    "get_method_definitions",
    "materialize_lines",
    "parse_datapipe_file",
    "parse_datapipe_files",
    "process_signature",
    "split_outside_bracket",
    # torch.utils.data.datapipes.map.callable
    "default_fn",
    # torch.utils.data.datapipes.utils.common
    "get_file_binaries_from_pathnames",
    "get_file_pathnames_from_root",
    "match_masks",
    "validate_input_col",
    "validate_pathname_binary_tuple",
    # torch.utils.data.datapipes.utils.decoder
    "audiohandler",
    "basichandlers",
    "extension_extract_fn",
    "handle_extension",
    "imagehandler",
    "mathandler",
    "videohandler",
    # torch.utils.data.dataset
    "random_split",
    # torch.utils.data.graph
    "traverse",
    "traverse_dps",
    # torch.utils.data.graph_settings
    "apply_random_seed",
    "apply_sharding",
    "apply_shuffle_seed",
    "apply_shuffle_settings",
    "get_all_graph_pipes",
    # torch.utils.flop_counter
    "addmm_flop",
    "baddbmm_flop",
    "bmm_flop",
    "conv_backward_flop",
    "conv_flop",
    "conv_flop_count",
    "convert_num_with_suffix",
    "get_shape",
    "get_suffix_str",
    "mm_flop",
    "normalize_tuple",
    "register_flop_formula",
    "sdpa_backward_flop",
    "sdpa_backward_flop_count",
    "sdpa_flop",
    "sdpa_flop_count",
    "shape_wrapper",
    "transpose_shape",
    # torch.utils.hipify.hipify_python
    "add_dim3",
    "compute_stats",
    "extract_arguments",
    "file_add_header",
    "file_specific_replacement",
    "find_bracket_group",
    "find_closure_group",
    "find_parentheses_group",
    "fix_static_global_kernels",
    "get_hip_file_path",
    "hip_header_magic",
    "hipify",
    "is_caffe2_gpu_file",
    "is_cusparse_file",
    "is_out_of_place",
    "is_pytorch_file",
    "is_special_file",
    "match_extensions",
    "matched_files_iter",
    "openf",
    "preprocess_file_and_save_result",
    "preprocessor",
    "processKernelLaunches",
    "replace_extern_shared",
    "replace_math_functions",
    "str2bool",
    # torch.utils.hooks
    "unserializable_hook",
    "warn_if_has_hooks",
    # torch.utils.jit.log_extract
    "extract_ir",
    "load_graph_and_inputs",
    "make_tensor_from_type",
    "no_fuser",
    "time_cpu",
    "time_cuda",
    # torch.utils.mkldnn
    "to_mkldnn",
    # torch.utils.mobile_optimizer
    "generate_mobile_module_lints",
    # torch.utils.tensorboard.summary
    "audio",
    "compute_curve",
    "custom_scalars",
    "draw_boxes",
    "half_to_int",
    "histogram",
    "histogram_raw",
    "hparams",
    "image",
    "image_boxes",
    "int_to_half",
    "make_histogram",
    "make_image",
    "make_video",
    "mesh",
    "pr_curve",
    "pr_curve_raw",
    "scalar",
    "tensor_proto",
    "text",
    "video",
    # torch.utils.throughput_benchmark
    "format_time",
]

coverage_ignore_classes = [
    # torch
    "FatalError",
    "QUInt2x4Storage",
    "Size",
    "Storage",
    "Stream",
    "Tensor",
    "finfo",
    "iinfo",
    "qscheme",
    "AggregationType",
    "AliasDb",
    "AnyType",
    "Argument",
    "ArgumentSpec",
    "AwaitType",
    "BenchmarkConfig",
    "BenchmarkExecutionStats",
    "Block",
    "BoolType",
    "BufferDict",
    "CallStack",
    "Capsule",
    "ClassType",
    "Code",
    "CompleteArgumentSpec",
    "ComplexType",
    "ConcreteModuleType",
    "ConcreteModuleTypeBuilder",
    "DeepCopyMemoTable",
    "DeserializationStorageContext",
    "DeviceObjType",
    "DictType",
    "DispatchKey",
    "DispatchKeySet",
    "EnumType",
    "ExcludeDispatchKeyGuard",
    "ExecutionPlan",
    "FileCheck",
    "FloatType",
    "FunctionSchema",
    "Gradient",
    "Graph",
    "GraphExecutorState",
    "IODescriptor",
    "InferredType",
    "IntType",
    "InterfaceType",
    "ListType",
    "LockingLogger",
    "MobileOptimizerType",
    "ModuleDict",
    "Node",
    "NoneType",
    "NoopLogger",
    "NumberType",
    "OperatorInfo",
    "OptionalType",
    "ParameterDict",
    "PyObjectType",
    "PyTorchFileReader",
    "PyTorchFileWriter",
    "RRefType",
    "ScriptClass",
    "ScriptClassFunction",
    "ScriptDict",
    "ScriptDictIterator",
    "ScriptDictKeyIterator",
    "ScriptList",
    "ScriptListIterator",
    "ScriptMethod",
    "ScriptModule",
    "ScriptModuleSerializer",
    "ScriptObject",
    "ScriptObjectProperty",
    "SerializationStorageContext",
    "StaticModule",
    "StringType",
    "SymIntType",
    "SymBoolType",
    "ThroughputBenchmark",
    "TracingState",
    "TupleType",
    "Type",
    "UnionType",
    "Use",
    "Value",
    # torch.cuda
    "BFloat16Storage",
    "BFloat16Tensor",
    "BoolStorage",
    "BoolTensor",
    "ByteStorage",
    "ByteTensor",
    "CharStorage",
    "CharTensor",
    "ComplexDoubleStorage",
    "ComplexFloatStorage",
    "CudaError",
    "DeferredCudaCallError",
    "DoubleStorage",
    "DoubleTensor",
    "FloatStorage",
    "FloatTensor",
    "HalfStorage",
    "HalfTensor",
    "IntStorage",
    "IntTensor",
    "LongStorage",
    "LongTensor",
    "ShortStorage",
    "ShortTensor",
    "cudaStatus",
    # torch.cuda._sanitizer
    "Access",
    "AccessType",
    "Await",
    "CUDASanitizer",
    "CUDASanitizerDispatchMode",
    "CUDASanitizerErrors",
    "EventHandler",
    "SynchronizationError",
    "UnsynchronizedAccessError",
    # torch.cuda.memory
    "MemPoolContext",
    # torch.distributed.elastic.multiprocessing.errors
    "ChildFailedError",
    "ProcessFailure",
    # torch.distributions.constraints
    "cat",
    "greater_than",
    "greater_than_eq",
    "half_open_interval",
    "independent",
    "integer_interval",
    "interval",
    "less_than",
    "multinomial",
    "stack",
    # torch.distributions.transforms
    "AffineTransform",
    "CatTransform",
    "ComposeTransform",
    "CorrCholeskyTransform",
    "CumulativeDistributionTransform",
    "ExpTransform",
    "IndependentTransform",
    "PowerTransform",
    "ReshapeTransform",
    "SigmoidTransform",
    "SoftmaxTransform",
    "SoftplusTransform",
    "StackTransform",
    "StickBreakingTransform",
    "TanhTransform",
    "Transform",
    # torch.jit
    "CompilationUnit",
    "Error",
    "Future",
    "ScriptFunction",
    # torch.onnx
    "CheckerError",
    "ExportTypes",
    # torch.backends
    "ContextProp",
    "PropModule",
    # torch.backends.cuda
    "cuBLASModule",
    "cuFFTPlanCache",
    "cuFFTPlanCacheAttrContextProp",
    "cuFFTPlanCacheManager",
    # torch.distributed.algorithms.ddp_comm_hooks
    "DDPCommHookType",
    # torch.jit.mobile
    "LiteScriptModule",
    # torch.ao.nn.quantized.modules
    "DeQuantize",
    "Quantize",
    # torch.utils.backcompat
    "Warning",
    # torch.ao.nn.intrinsic.modules.fused
    "ConvAdd2d",
    "ConvAddReLU2d",
    "LinearBn1d",
    "LinearLeakyReLU",
    "LinearTanh",
    # torch.ao.nn.intrinsic.qat.modules.conv_fused
    "ConvBnReLU1d",
    "ConvBnReLU2d",
    "ConvBnReLU3d",
    "ConvReLU1d",
    "ConvReLU2d",
    "ConvReLU3d",
    # torch.ao.nn.intrinsic.qat.modules.linear_fused
    "LinearBn1d",
    # torch.ao.nn.intrinsic.qat.modules.linear_relu
    "LinearReLU",
    # torch.ao.nn.intrinsic.quantized.dynamic.modules.linear_relu
    "LinearReLU",
    # torch.ao.nn.intrinsic.quantized.modules.bn_relu
    "BNReLU2d",
    "BNReLU3d",
    # torch.ao.nn.intrinsic.quantized.modules.conv_add
    "ConvAdd2d",
    "ConvAddReLU2d",
    # torch.ao.nn.intrinsic.quantized.modules.conv_relu
    "ConvReLU1d",
    "ConvReLU2d",
    "ConvReLU3d",
    # torch.ao.nn.intrinsic.quantized.modules.linear_relu
    "LinearLeakyReLU",
    "LinearReLU",
    "LinearTanh",
    # torch.ao.nn.qat.modules.conv
    "Conv1d",
    "Conv2d",
    "Conv3d",
    # torch.ao.nn.qat.modules.embedding_ops
    "Embedding",
    "EmbeddingBag",
    # torch.ao.nn.qat.modules.linear
    "Linear",
    # torch.ao.nn.quantizable.modules.activation
    "MultiheadAttention",
    # torch.ao.nn.quantizable.modules.rnn
    "LSTM",
    "LSTMCell",
    # torch.ao.nn.quantized.dynamic.modules.conv
    "Conv1d",
    "Conv2d",
    "Conv3d",
    "ConvTranspose1d",
    "ConvTranspose2d",
    "ConvTranspose3d",
    # torch.ao.nn.quantized.dynamic.modules.linear
    "Linear",
    # torch.ao.nn.quantized.dynamic.modules.rnn
    "GRU",
    "GRUCell",
    "LSTM",
    "LSTMCell",
    "PackedParameter",
    "RNNBase",
    "RNNCell",
    "RNNCellBase",
    # torch.ao.nn.quantized.modules.activation
    "ELU",
    "Hardswish",
    "LeakyReLU",
    "MultiheadAttention",
    "PReLU",
    "ReLU6",
    "Sigmoid",
    "Softmax",
    # torch.ao.nn.quantized.modules.batchnorm
    "BatchNorm2d",
    "BatchNorm3d",
    # torch.ao.nn.quantized.modules.conv
    "Conv1d",
    "Conv2d",
    "Conv3d",
    "ConvTranspose1d",
    "ConvTranspose2d",
    "ConvTranspose3d",
    # torch.ao.nn.quantized.modules.dropout
    "Dropout",
    # torch.ao.nn.quantized.modules.embedding_ops
    "Embedding",
    "EmbeddingBag",
    "EmbeddingPackedParams",
    # torch.ao.nn.quantized.modules.functional_modules
    "FXFloatFunctional",
    "FloatFunctional",
    "QFunctional",
    # torch.ao.nn.quantized.modules.linear
    "Linear",
    "LinearPackedParams",
    # torch.ao.nn.quantized.modules.normalization
    "GroupNorm",
    "InstanceNorm1d",
    "InstanceNorm2d",
    "InstanceNorm3d",
    "LayerNorm",
    # torch.ao.nn.quantized.modules.rnn
    "LSTM",
    # torch.ao.nn.quantized.modules.utils
    "WeightedQuantizedModule",
    # torch.ao.nn.quantized.reference.modules.conv
    "Conv1d",
    "Conv2d",
    "Conv3d",
    "ConvTranspose1d",
    "ConvTranspose2d",
    "ConvTranspose3d",
    # torch.ao.nn.quantized.reference.modules.linear
    "Linear",
    # torch.ao.nn.quantized.reference.modules.rnn
    "GRU",
    "GRUCell",
    "LSTM",
    "LSTMCell",
    "RNNBase",
    "RNNCell",
    "RNNCellBase",
    # torch.ao.nn.quantized.reference.modules.sparse
    "Embedding",
    "EmbeddingBag",
    # torch.ao.nn.quantized.reference.modules.utils
    "ReferenceQuantizedModule",
    # torch.ao.nn.sparse.quantized.dynamic.linear
    "Linear",
    # torch.ao.nn.sparse.quantized.linear
    "Linear",
    "LinearPackedParams",
    # torch.ao.nn.sparse.quantized.utils
    "LinearBlockSparsePattern",
    # torch.ao.ns.fx.graph_matcher
    "SubgraphTypeRelationship",
    # torch.ao.ns.fx.n_shadows_utils
    "OutputProp",
    # torch.ao.ns.fx.ns_types
    "NSSingleResultValuesType",
    "NSSubgraph",
    # torch.ao.ns.fx.qconfig_multi_mapping
    "QConfigMultiMapping",
    # torch.ao.pruning.scheduler.base_scheduler
    "BaseScheduler",
    # torch.ao.pruning.scheduler.cubic_scheduler
    "CubicSL",
    # torch.ao.pruning.scheduler.lambda_scheduler
    "LambdaSL",
    # torch.ao.pruning.sparsifier.base_sparsifier
    "BaseSparsifier",
    # torch.ao.pruning.sparsifier.nearly_diagonal_sparsifier
    "NearlyDiagonalSparsifier",
    # torch.ao.pruning.sparsifier.utils
    "FakeSparsity",
    # torch.ao.pruning.sparsifier.weight_norm_sparsifier
    "WeightNormSparsifier",
    # torch.ao.quantization.backend_config.backend_config
    "BackendConfig",
    "BackendPatternConfig",
    "DTypeConfig",
    # torch.ao.quantization.fake_quantize
    "FakeQuantize",
    "FakeQuantizeBase",
    "FixedQParamsFakeQuantize",
    "FusedMovingAvgObsFakeQuantize",
    # torch.ao.quantization.fx.fuse_handler
    "DefaultFuseHandler",
    "FuseHandler",
    # torch.ao.quantization.fx.graph_module
    "FusedGraphModule",
    "ObservedGraphModule",
    "ObservedStandaloneGraphModule",
    # torch.ao.quantization.fx.quantize_handler
    "BatchNormQuantizeHandler",
    "BinaryOpQuantizeHandler",
    "CatQuantizeHandler",
    "ConvReluQuantizeHandler",
    "CopyNodeQuantizeHandler",
    "CustomModuleQuantizeHandler",
    "DefaultNodeQuantizeHandler",
    "EmbeddingQuantizeHandler",
    "FixedQParamsOpQuantizeHandler",
    "GeneralTensorShapeOpQuantizeHandler",
    "LinearReLUQuantizeHandler",
    "RNNDynamicQuantizeHandler",
    "StandaloneModuleQuantizeHandler",
    # torch.ao.quantization.fx.tracer
    "QuantizationTracer",
    "ScopeContextManager",
    # torch.ao.quantization.fx.utils
    "ObservedGraphModuleAttrs",
    # torch.ao.quantization.observer
    "FixedQParamsObserver",
    "HistogramObserver",
    "MinMaxObserver",
    "MovingAverageMinMaxObserver",
    "MovingAveragePerChannelMinMaxObserver",
    "NoopObserver",
    "ObserverBase",
    "PerChannelMinMaxObserver",
    "PlaceholderObserver",
    "RecordingObserver",
    "ReuseInputObserver",
    "UniformQuantizationObserverBase",
    "default_debug_observer",
    "default_placeholder_observer",
    "default_reuse_input_observer",
    # torch.ao.quantization.pt2e.duplicate_dq_pass
    "DuplicateDQPass",
    # torch.ao.quantization.pt2e.port_metadata_pass
    "PortNodeMetaForQDQ",
    # torch.ao.quantization.qconfig
    "QConfigDynamic",
    # torch.ao.quantization.quant_type
    "QuantType",
    # torch.ao.quantization.quantizer.composable_quantizer
    "ComposableQuantizer",
    # torch.ao.quantization.quantizer.embedding_quantizer
    "EmbeddingQuantizer",
    # torch.ao.quantization.quantizer.quantizer
    "DerivedQuantizationSpec",
    "FixedQParamsQuantizationSpec",
    "QuantizationAnnotation",
    "QuantizationSpec",
    "QuantizationSpecBase",
    "SharedQuantizationSpec",
    # torch.ao.quantization.quantizer.x86_inductor_quantizer
    "X86InductorQuantizer",
    # torch.ao.quantization.quantizer.xnnpack_quantizer
    "XNNPACKQuantizer",
    # torch.ao.quantization.quantizer.xnnpack_quantizer_utils
    "OperatorConfig",
    "QuantizationConfig",
    # torch.ao.quantization.stubs
    "DeQuantStub",
    "QuantStub",
    "QuantWrapper",
    # torch.ao.quantization.utils
    "MatchAllNode",
    # torch.backends.cudnn.rnn
    "Unserializable",
    # torch.amp.grad_scaler
    "GradScaler",
    "OptState",
    # torch.cuda.graphs
    "CUDAGraph",
    # torch.cuda.streams
    "Event",
    # torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook
    "PostLocalSGDState",
    # torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook
    "PowerSGDState",
    # torch.distributed.algorithms.join
    "Join",
    "JoinHook",
    "Joinable",
    # torch.distributed.algorithms.model_averaging.averagers
    "ModelAverager",
    "PeriodicModelAverager",
    # torch.distributed.algorithms.model_averaging.hierarchical_model_averager
    "HierarchicalModelAverager",
    # torch.distributed.argparse_util
    "check_env",
    "env",
    # torch.distributed.checkpoint.api
    "CheckpointException",
    # torch.distributed.checkpoint.default_planner
    "DefaultLoadPlanner",
    "DefaultSavePlanner",
    # torch.distributed.checkpoint.filesystem
    "FileSystemReader",
    "FileSystemWriter",
    # torch.distributed.checkpoint.metadata
    "BytesStorageMetadata",
    "ChunkStorageMetadata",
    "Metadata",
    "MetadataIndex",
    # torch.distributed.checkpoint.planner
    "LoadItemType",
    "LoadPlanner",
    "SavePlanner",
    "WriteItemType",
    # torch.distributed.checkpoint.state_dict
    "DistributedStateDictOptions",
    # torch.distributed.checkpoint.storage
    "WriteResult",
    # torch.distributed.collective_utils
    "SyncPayload",
    # torch.distributed.distributed_c10d
    "AllToAllOptions",
    "AllreduceCoalescedOptions",
    "AllreduceOptions",
    "Backend",
    "BackendConfig",
    "BarrierOptions",
    "BroadcastOptions",
    "DebugLevel",
    "GatherOptions",
    "GroupMember",
    "ProcessGroup",
    "ProcessGroupGloo",
    "ProcessGroupNCCL",
    "ReduceOptions",
    "ReduceScatterOptions",
    "ScatterOptions",
    "Work",
    "group",
    # torch.distributed.elastic.agent.server.api
    "ElasticAgent",
    "RunResult",
    "SimpleElasticAgent",
    "WorkerSpec",
    # torch.distributed.elastic.events.api
    "Event",
    "RdzvEvent",
    # torch.distributed.elastic.metrics.api
    "ConsoleMetricHandler",
    "MetricData",
    "MetricHandler",
    "MetricStream",
    "MetricsConfig",
    "NullMetricHandler",
    # torch.distributed.elastic.multiprocessing.api
    "MultiprocessContext",
    "PContext",
    "RunProcsResult",
    "SignalException",
    "Std",
    "SubprocessContext",
    "SubprocessHandler",
    # torch.distributed.elastic.multiprocessing.tail_log
    "TailLog",
    # torch.distributed.elastic.rendezvous.api
    "RendezvousHandler",
    "RendezvousHandlerRegistry",
    "RendezvousParameters",
    # torch.distributed.elastic.rendezvous.dynamic_rendezvous
    "DynamicRendezvousHandler",
    "RendezvousSettings",
    # torch.distributed.elastic.rendezvous.etcd_rendezvous
    "EtcdRendezvous",
    "EtcdRendezvousHandler",
    "EtcdRendezvousRetryImmediately",
    "EtcdRendezvousRetryableFailure",
    # torch.distributed.elastic.rendezvous.etcd_server
    "EtcdServer",
    # torch.distributed.elastic.rendezvous.static_tcp_rendezvous
    "StaticTCPRendezvous",
    # torch.distributed.elastic.timer.api
    "RequestQueue",
    "TimerClient",
    "TimerServer",
    # torch.distributed.elastic.timer.file_based_local_timer
    "FileTimerClient",
    "FileTimerRequest",
    "FileTimerServer",
    # torch.distributed.elastic.timer.local_timer
    "LocalTimerClient",
    "LocalTimerServer",
    "MultiprocessingRequestQueue",
    # torch.distributed.elastic.utils.api
    "macros",
    # torch.distributed.elastic.utils.data.cycling_iterator
    "CyclingIterator",
    # torch.distributed.elastic.utils.data.elastic_distributed_sampler
    "ElasticDistributedSampler",
    # torch.distributed.fsdp.api
    "StateDictType",
    # torch.distributed.fsdp.fully_sharded_data_parallel
    "FullyShardedDataParallel",
    "OptimStateKeyType",
    # torch.distributed.fsdp.sharded_grad_scaler
    "ShardedGradScaler",
    # torch.distributed.fsdp.wrap
    "CustomPolicy",
    "ModuleWrapPolicy",
    # torch.distributed.launcher.api
    "LaunchConfig",
    "elastic_launch",
    # torch.distributed.optim.optimizer
    "DistributedOptimizer",
    # torch.distributed.optim.post_localSGD_optimizer
    "PostLocalSGDOptimizer",
    # torch.distributed.optim.zero_redundancy_optimizer
    "ZeroRedundancyOptimizer",
    # torch.distributed.rpc.api
    "AllGatherStates",
    "RRef",
    # torch.distributed.rpc.backend_registry
    "BackendValue",
    # torch.distributed.rpc.internal
    "PythonUDF",
    "RPCExecMode",
    "RemoteException",
    # torch.distributed.rpc.rref_proxy
    "RRefProxy",
    # torch.distributed.tensor.parallel.fsdp
    "DTensorExtensions",
    # torch.distributed.tensor.parallel.style
    "ParallelStyle",
    # torch.distributions.logistic_normal
    "LogisticNormal",
    # torch.distributions.one_hot_categorical
    "OneHotCategoricalStraightThrough",
    # torch.distributions.relaxed_categorical
    "ExpRelaxedCategorical",
    # torch.distributions.utils
    "lazy_property",
    # torch.export.exported_program
    "ConstantArgument",
    "ExportedProgram",
    # torch.fx.experimental.accelerator_partitioner
    "DAG",
    "DAGNode",
    "PartitionResult",
    "Partitioner",
    # torch.fx.experimental.const_fold
    "FoldedGraphModule",
    # torch.fx.experimental.graph_gradual_typechecker
    "Refine",
    # torch.fx.experimental.meta_tracer
    "MetaAttribute",
    "MetaDeviceAttribute",
    "MetaProxy",
    "MetaTracer",
    # torch.fx.experimental.migrate_gradual_types.constraint
    "ApplyBroadcasting",
    "BVar",
    "BinConstraintD",
    "BinConstraintT",
    "BinaryConstraint",
    "CalcConv",
    "CalcMaxPool",
    "CalcProduct",
    "CanReshape",
    "Conj",
    "Constraint",
    "DGreatestUpperBound",
    "DVar",
    "Disj",
    "F",
    "GetItem",
    "GetItemTensor",
    "IndexSelect",
    "Prod",
    "T",
    "TGreatestUpperBound",
    "TVar",
    "Transpose",
    # torch.fx.experimental.migrate_gradual_types.constraint_generator
    "ConstraintGenerator",
    # torch.fx.experimental.normalize
    "NormalizeArgs",
    "NormalizeOperators",
    # torch.fx.experimental.optimization
    "MklSubgraph",
    "UnionFind",
    # torch.fx.experimental.partitioner_utils
    "Device",
    "Partition",
    "PartitionLatency",
    "PartitionMode",
    "PartitionerConfig",
    # torch.fx.experimental.proxy_tensor
    "DecompositionInterpreter",
    "PreDispatchTorchFunctionMode",
    "ProxySymDispatchMode",
    "ProxyTorchDispatchMode",
    "PythonKeyTracer",
    # torch.fx.experimental.recording
    "FakeTensorMeta",
    "NotEqualError",
    "ShapeEnvEvent",
    # torch.fx.experimental.refinement_types
    "Equality",
    # torch.fx.experimental.rewriter
    "AST_Rewriter",
    "RewritingTracer",
    # torch.fx.experimental.schema_type_annotation
    "AnnotateTypesWithSchema",
    # torch.fx.experimental.sym_node
    "SymNode",
    # torch.fx.experimental.symbolic_shapes
    "Constraint",
    "ConstraintViolationError",
    "DynamicDimConstraintPrinter",
    "GuardOnDataDependentSymNode",
    "PendingUnbackedSymbolNotFound",
    "LoggingShapeGuardPrinter",
    "SymExprPrinter",
    "RelaxedUnspecConstraint",
    "RuntimeAssert",
    "ShapeGuardPrinter",
    "SymDispatchMode",
    "SymbolicContext",
    # torch.fx.experimental.unification.match
    "Dispatcher",
    "VarDispatcher",
    # torch.fx.experimental.unification.multipledispatch.conflict
    "AmbiguityWarning",
    # torch.fx.experimental.unification.multipledispatch.dispatcher
    "Dispatcher",
    "MDNotImplementedError",
    "MethodDispatcher",
    # torch.fx.experimental.unification.multipledispatch.variadic
    "Variadic",
    "VariadicSignatureMeta",
    "VariadicSignatureType",
    # torch.fx.experimental.unification.variable
    "Var",
    # torch.fx.experimental.validator
    "BisectValidationException",
    "PopulateValidator",
    "SympyToZ3",
    "ValidationException",
    # torch.fx.graph
    "PythonCode",
    # torch.fx.immutable_collections
    "immutable_dict",
    "immutable_list",
    # torch.fx.interpreter
    "Interpreter",
    # torch.fx.operator_schemas
    "ArgsKwargsPair",
    # torch.fx.passes.backends.cudagraphs
    "CudaGraphsSupport",
    # torch.fx.passes.dialect.common.cse_pass
    "CSEPass",
    # torch.fx.passes.fake_tensor_prop
    "FakeTensorProp",
    # torch.fx.passes.graph_drawer
    "FxGraphDrawer",
    # torch.fx.passes.graph_manipulation
    "size_bytes",
    # torch.fx.passes.infra.partitioner
    "CapabilityBasedPartitioner",
    "Partition",
    # torch.fx.passes.infra.pass_base
    "PassBase",
    "PassResult",
    # torch.fx.passes.infra.pass_manager
    "PassManager",
    # torch.fx.passes.net_min_base
    "FxNetMinimizerBadModuleError",
    "FxNetMinimizerResultMismatchError",
    "FxNetMinimizerRunFuncError",
    # torch.fx.passes.operator_support
    "OpSupports",
    "OperatorSupport",
    "OperatorSupportBase",
    # torch.fx.passes.pass_manager
    "PassManager",
    # torch.fx.passes.shape_prop
    "ShapeProp",
    # torch.fx.passes.split_module
    "Partition",
    # torch.fx.passes.split_utils
    "Component",
    # torch.fx.passes.splitter_base
    "FxNetAccNodesFinder",
    "FxNetSplitterInternalError",
    "SplitResult",
    "Subgraph",
    # torch.fx.passes.tests.test_pass_manager
    "TestPassManager",
    # torch.fx.passes.tools_common
    "FxNetAccFusionsFinder",
    # torch.fx.passes.utils.common
    "HolderModule",
    # torch.fx.passes.utils.matcher_utils
    "InternalMatch",
    "SubgraphMatcher",
    # torch.fx.passes.utils.source_matcher_utils
    "SourcePartition",
    # torch.fx.proxy
    "Attribute",
    "ParameterProxy",
    "Proxy",
    "Scope",
    "ScopeContextManager",
    "TraceError",
    "TracerBase",
    # torch.fx.subgraph_rewriter
    "Match",
    "ReplacedPatterns",
    # torch.jit.annotations
    "EvalEnv",
    "Module",
    # torch.jit.frontend
    "Builder",
    "ExprBuilder",
    "FrontendError",
    "FrontendTypeError",
    "NotSupportedError",
    "StmtBuilder",
    "UnsupportedNodeError",
    "WithItemBuilder",
    # torch.masked.maskedtensor.core
    "MaskedTensor",
    # torch.multiprocessing.pool
    "Pool",
    # torch.multiprocessing.queue
    "ConnectionWrapper",
    "Queue",
    "SimpleQueue",
    # torch.multiprocessing.reductions
    "SharedCache",
    # torch.multiprocessing.spawn
    "ProcessContext",
    "ProcessException",
    "ProcessExitedException",
    "ProcessRaisedException",
    "SpawnContext",
    # torch.nn.cpp
    "ModuleWrapper",
    "OrderedDictWrapper",
    # torch.nn.modules.activation
    "CELU",
    "ELU",
    "GELU",
    "GLU",
    "Hardshrink",
    "Hardsigmoid",
    "Hardswish",
    "Hardtanh",
    "LeakyReLU",
    "LogSigmoid",
    "LogSoftmax",
    "Mish",
    "MultiheadAttention",
    "PReLU",
    "RReLU",
    "ReLU",
    "ReLU6",
    "SELU",
    "SiLU",
    "Sigmoid",
    "Softmax",
    "Softmax2d",
    "Softmin",
    "Softplus",
    "Softshrink",
    "Softsign",
    "Tanh",
    "Tanhshrink",
    "Threshold",
    # torch.nn.modules.adaptive
    "AdaptiveLogSoftmaxWithLoss",
    # torch.nn.modules.batchnorm
    "SyncBatchNorm",
    # torch.nn.modules.channelshuffle
    "ChannelShuffle",
    # torch.nn.modules.container
    "Container",
    "ModuleList",
    "ParameterList",
    "Sequential",
    # torch.nn.modules.conv
    "Conv1d",
    "Conv2d",
    "Conv3d",
    "ConvTranspose1d",
    "ConvTranspose2d",
    "ConvTranspose3d",
    # torch.nn.modules.distance
    "CosineSimilarity",
    "PairwiseDistance",
    # torch.nn.modules.dropout
    "AlphaDropout",
    "Dropout",
    "Dropout1d",
    "Dropout2d",
    "Dropout3d",
    "FeatureAlphaDropout",
    # torch.nn.modules.flatten
    "Flatten",
    "Unflatten",
    # torch.nn.modules.fold
    "Fold",
    "Unfold",
    # torch.nn.modules.linear
    "Bilinear",
    "Identity",
    "LazyLinear",
    "Linear",
    "NonDynamicallyQuantizableLinear",
    # torch.nn.modules.loss
    "BCELoss",
    "BCEWithLogitsLoss",
    "CTCLoss",
    "CosineEmbeddingLoss",
    "CrossEntropyLoss",
    "GaussianNLLLoss",
    "HingeEmbeddingLoss",
    "HuberLoss",
    "KLDivLoss",
    "L1Loss",
    "MSELoss",
    "MarginRankingLoss",
    "MultiLabelMarginLoss",
    "MultiLabelSoftMarginLoss",
    "MultiMarginLoss",
    "NLLLoss",
    "NLLLoss2d",
    "PoissonNLLLoss",
    "SmoothL1Loss",
    "SoftMarginLoss",
    "TripletMarginLoss",
    "TripletMarginWithDistanceLoss",
    # torch.nn.modules.module
    "Module",
    # torch.nn.modules.normalization
    "CrossMapLRN2d",
    "GroupNorm",
    "LayerNorm",
    "LocalResponseNorm",
    # torch.nn.modules.padding
    "CircularPad1d",
    "CircularPad2d",
    "CircularPad3d",
    "ZeroPad1d",
    "ZeroPad2d",
    "ZeroPad3d",
    # torch.nn.modules.pixelshuffle
    "PixelShuffle",
    "PixelUnshuffle",
    # torch.nn.modules.pooling
    "AdaptiveAvgPool1d",
    "AdaptiveAvgPool2d",
    "AdaptiveAvgPool3d",
    "AdaptiveMaxPool1d",
    "AdaptiveMaxPool2d",
    "AdaptiveMaxPool3d",
    "AvgPool1d",
    "AvgPool2d",
    "AvgPool3d",
    "FractionalMaxPool2d",
    "FractionalMaxPool3d",
    "LPPool1d",
    "LPPool2d",
    "LPPool3d",
    "MaxPool1d",
    "MaxPool2d",
    "MaxPool3d",
    "MaxUnpool1d",
    "MaxUnpool2d",
    "MaxUnpool3d",
    # torch.nn.modules.rnn
    "GRU",
    "GRUCell",
    "LSTM",
    "LSTMCell",
    "RNN",
    "RNNBase",
    "RNNCell",
    "RNNCellBase",
    # torch.nn.modules.sparse
    "Embedding",
    "EmbeddingBag",
    # torch.nn.modules.upsampling
    "Upsample",
    # torch.nn.parallel.data_parallel
    "DataParallel",
    # torch.nn.parallel.distributed
    "DistributedDataParallel",
    # torch.nn.parameter
    "UninitializedTensorMixin",
    # torch.nn.utils.parametrize
    "ParametrizationList",
    # torch.nn.utils.prune
    "CustomFromMask",
    "Identity",
    "L1Unstructured",
    "RandomUnstructured",
    # torch.nn.utils.rnn
    "PackedSequence",
    "PackedSequence_",
    # torch.nn.utils.spectral_norm
    "SpectralNorm",
    "SpectralNormLoadStateDictPreHook",
    "SpectralNormStateDictHook",
    # torch.nn.utils.weight_norm
    "WeightNorm",
    # torch.onnx.errors
    "OnnxExporterError",
    "OnnxExporterWarning",
    "SymbolicValueError",
    "UnsupportedOperatorError",
    # torch.onnx.verification
    "OnnxBackend",
    "OnnxTestCaseRepro",
    # torch.optim.adadelta
    "Adadelta",
    # torch.optim.adagrad
    "Adagrad",
    # torch.optim.adam
    "Adam",
    # torch.optim.adamax
    "Adamax",
    # torch.optim.adamw
    "AdamW",
    # torch.optim.asgd
    "ASGD",
    # torch.optim.lbfgs
    "LBFGS",
    # torch.optim.lr_scheduler
    "ChainedScheduler",
    "ConstantLR",
    "CosineAnnealingLR",
    "CosineAnnealingWarmRestarts",
    "CyclicLR",
    "ExponentialLR",
    "LRScheduler",
    "LambdaLR",
    "LinearLR",
    "MultiStepLR",
    "MultiplicativeLR",
    "OneCycleLR",
    "PolynomialLR",
    "ReduceLROnPlateau",
    "SequentialLR",
    "StepLR",
    # torch.optim.nadam
    "NAdam",
    # torch.optim.optimizer
    "Optimizer",
    # torch.optim.radam
    "RAdam",
    # torch.optim.rmsprop
    "RMSprop",
    # torch.optim.rprop
    "Rprop",
    # torch.optim.sgd
    "SGD",
    # torch.optim.sparse_adam
    "SparseAdam",
    # torch.optim.swa_utils
    "AveragedModel",
    "SWALR",
    # torch.overrides
    "BaseTorchFunctionMode",
    "TorchFunctionMode",
    # torch.package.file_structure_representation
    "Directory",
    # torch.package.glob_group
    "GlobGroup",
    # torch.package.importer
    "Importer",
    "ObjMismatchError",
    "ObjNotFoundError",
    "OrderedImporter",
    # torch.package.package_exporter
    "PackageExporter",
    "PackagingErrorReason",
    # torch.package.package_importer
    "PackageImporter",
    # torch.profiler.profiler
    "ExecutionTraceObserver",
    "profile",
    # torch.return_types
    "aminmax",
    "aminmax_out",
    "cummax",
    "cummax_out",
    "cummin",
    "cummin_out",
    "frexp",
    "frexp_out",
    "geqrf",
    "geqrf_out",
    "histogram",
    "histogram_out",
    "histogramdd",
    "kthvalue",
    "kthvalue_out",
    "linalg_cholesky_ex",
    "linalg_cholesky_ex_out",
    "linalg_eig",
    "linalg_eig_out",
    "linalg_eigh",
    "linalg_eigh_out",
    "linalg_inv_ex",
    "linalg_inv_ex_out",
    "linalg_ldl_factor",
    "linalg_ldl_factor_ex",
    "linalg_ldl_factor_ex_out",
    "linalg_ldl_factor_out",
    "linalg_lstsq",
    "linalg_lstsq_out",
    "linalg_lu",
    "linalg_lu_factor",
    "linalg_lu_factor_ex",
    "linalg_lu_factor_ex_out",
    "linalg_lu_factor_out",
    "linalg_lu_out",
    "linalg_qr",
    "linalg_qr_out",
    "linalg_slogdet",
    "linalg_slogdet_out",
    "linalg_solve_ex",
    "linalg_solve_ex_out",
    "linalg_svd",
    "linalg_svd_out",
    "lu_unpack",
    "lu_unpack_out",
    "max",
    "max_out",
    "median",
    "median_out",
    "min",
    "min_out",
    "mode",
    "mode_out",
    "nanmedian",
    "nanmedian_out",
    "qr",
    "qr_out",
    "slogdet",
    "slogdet_out",
    "sort",
    "sort_out",
    "svd",
    "svd_out",
    "topk",
    "topk_out",
    "triangular_solve",
    "triangular_solve_out",
    # torch.serialization
    "LoadEndianness",
    "SourceChangeWarning",
    # torch.sparse.semi_structured
    "SparseSemiStructuredTensor",
    # torch.storage
    "UntypedStorage",
    # torch.torch_version
    "TorchVersion",
    # torch.types
    "SymInt",
    # torch.utils.benchmark.examples.blas_compare_setup
    "SubEnvSpec",
    # torch.utils.benchmark.examples.compare
    "FauxTorch",
    # torch.utils.benchmark.examples.spectral_ops_fuzz_test
    "Benchmark",
    # torch.utils.benchmark.op_fuzzers.binary
    "BinaryOpFuzzer",
    # torch.utils.benchmark.op_fuzzers.sparse_binary
    "BinaryOpSparseFuzzer",
    # torch.utils.benchmark.op_fuzzers.sparse_unary
    "UnaryOpSparseFuzzer",
    # torch.utils.benchmark.op_fuzzers.spectral
    "SpectralOpFuzzer",
    # torch.utils.benchmark.op_fuzzers.unary
    "UnaryOpFuzzer",
    # torch.utils.benchmark.utils.common
    "Measurement",
    "TaskSpec",
    # torch.utils.benchmark.utils.compare
    "Colorize",
    "Compare",
    "Table",
    # torch.utils.benchmark.utils.fuzzer
    "FuzzedParameter",
    "FuzzedTensor",
    "Fuzzer",
    "ParameterAlias",
    # torch.utils.benchmark.utils.sparse_fuzzer
    "FuzzedSparseTensor",
    # torch.utils.benchmark.utils.timer
    "CPPTimer",
    "Language",
    "Timer",
    # torch.utils.benchmark.utils.valgrind_wrapper.timer_interface
    "CallgrindStats",
    "CopyIfCallgrind",
    "FunctionCount",
    "FunctionCounts",
    "GlobalsBridge",
    "Serialization",
    # torch.utils.bundled_inputs
    "InflatableArg",
    # torch.utils.checkpoint
    "CheckpointError",
    "CheckpointFunction",
    "DefaultDeviceType",
    # torch.utils.collect_env
    "SystemEnv",
    # torch.utils.cpp_extension
    "BuildExtension",
    # torch.utils.data.dataloader
    "DataLoader",
    # torch.utils.data.datapipes.dataframe.dataframe_wrapper
    "PandasWrapper",
    "default_wrapper",
    # torch.utils.data.datapipes.dataframe.dataframes
    "Capture",
    "CaptureA",
    "CaptureAdd",
    "CaptureCall",
    "CaptureControl",
    "CaptureDataFrame",
    "CaptureDataFrameWithDataPipeOps",
    "CaptureF",
    "CaptureGetAttr",
    "CaptureGetItem",
    "CaptureInitial",
    "CaptureLikeMock",
    "CaptureMul",
    "CaptureSetItem",
    "CaptureSub",
    "CaptureVariable",
    "CaptureVariableAssign",
    "DataFrameTracedOps",
    "DataFrameTracer",
    # torch.utils.data.datapipes.dataframe.datapipes
    "ConcatDataFramesPipe",
    "DataFramesAsTuplesPipe",
    "ExampleAggregateAsDataFrames",
    "FilterDataFramesPipe",
    "PerRowDataFramesPipe",
    "ShuffleDataFramesPipe",
    # torch.utils.data.datapipes.dataframe.structures
    "DataChunkDF",
    # torch.utils.data.datapipes.datapipe
    "DFIterDataPipe",
    "DataChunk",
    "IterDataPipe",
    "MapDataPipe",
    # torch.utils.data.datapipes.iter.callable
    "CollatorIterDataPipe",
    "MapperIterDataPipe",
    # torch.utils.data.datapipes.iter.combinatorics
    "SamplerIterDataPipe",
    "ShufflerIterDataPipe",
    # torch.utils.data.datapipes.iter.combining
    "ConcaterIterDataPipe",
    "DemultiplexerIterDataPipe",
    "ForkerIterDataPipe",
    "MultiplexerIterDataPipe",
    "ZipperIterDataPipe",
    # torch.utils.data.datapipes.iter.filelister
    "FileListerIterDataPipe",
    # torch.utils.data.datapipes.iter.fileopener
    "FileOpenerIterDataPipe",
    # torch.utils.data.datapipes.iter.grouping
    "BatcherIterDataPipe",
    "GrouperIterDataPipe",
    "UnBatcherIterDataPipe",
    # torch.utils.data.datapipes.iter.routeddecoder
    "RoutedDecoderIterDataPipe",
    # torch.utils.data.datapipes.iter.selecting
    "FilterIterDataPipe",
    # torch.utils.data.datapipes.iter.sharding
    "SHARDING_PRIORITIES",
    "ShardingFilterIterDataPipe",
    # torch.utils.data.datapipes.iter.utils
    "IterableWrapperIterDataPipe",
    # torch.utils.data.datapipes.map.callable
    "MapperMapDataPipe",
    # torch.utils.data.datapipes.map.combinatorics
    "ShufflerIterDataPipe",
    # torch.utils.data.datapipes.map.combining
    "ConcaterMapDataPipe",
    "ZipperMapDataPipe",
    # torch.utils.data.datapipes.map.grouping
    "BatcherMapDataPipe",
    # torch.utils.data.datapipes.map.utils
    "SequenceWrapperMapDataPipe",
    # torch.utils.data.datapipes.utils.decoder
    "Decoder",
    "ImageHandler",
    "MatHandler",
    # torch.utils.data.dataset
    "ConcatDataset",
    # torch.utils.data.distributed
    "DistributedSampler",
    # torch.utils.dlpack
    "DLDeviceType",
    # torch.utils.file_baton
    "FileBaton",
    # torch.utils.flop_counter
    "FlopCounterMode",
    # torch.utils.hipify.hipify_python
    "CurrentState",
    "GeneratedFileCleaner",
    "HipifyResult",
    "InputError",
    "Trie",
    "bcolors",
    # torch.utils.hooks
    "BackwardHook",
    "RemovableHandle",
    # torch.utils.mkldnn
    "MkldnnBatchNorm",
    "MkldnnConv1d",
    "MkldnnConv2d",
    "MkldnnConv3d",
    "MkldnnLinear",
    "MkldnnPrelu",
    # torch.utils.mobile_optimizer
    "LintCode",
    # torch.utils.show_pickle
    "DumpUnpickler",
    "FakeClass",
    "FakeObject",
    # torch.utils.tensorboard.writer
    "FileWriter",
    "SummaryWriter",
    # torch.utils.throughput_benchmark
    "ExecutionStats",
    # torch.utils.weak
    "WeakIdKeyDictionary",
    "WeakIdRef",
    "WeakTensorKeyDictionary",
]

# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = ".rst"

# The master toctree document.
master_doc = "index"

# General information about the project.
project = "PyTorch"
copyright = "2023, PyTorch Contributors"
author = "PyTorch Contributors"
torch_version = str(torch.__version__)

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
# TODO: change to [:2] at v1.0
version = "main (" + torch_version + " )"
# The full version, including alpha/beta/rc tags.
# TODO: verify this works as expected
release = "main"

# Customized html_title here.
# Default is " ".join(project, release, "documentation") if not set
if RELEASE:
    # Turn 1.11.0aHASH into 1.11
    # Note: the release candidates should no longer have the aHASH suffix, but in any
    # case we wish to leave only major.minor, even for rc builds.
    version = ".".join(torch_version.split(".")[:2])
    html_title = " ".join((project, version, "documentation"))
    release = version

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = "en"

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = []

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = "sphinx"

# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = True

# Disable docstring inheritance
autodoc_inherit_docstrings = False

# Show type hints in the description
autodoc_typehints = "description"

# Add parameter types if the parameter is documented in the docstring
autodoc_typehints_description_target = "documented_params"

# Type aliases for common types
# Sphinx type aliases only works with Postponed Evaluation of Annotations
# (PEP 563) enabled (via `from __future__ import annotations`), which keeps the
# type annotations in string form instead of resolving them to actual types.
# However, PEP 563 does not work well with JIT, which uses the type information
# to generate the code. Therefore, the following dict does not have any effect
# until PEP 563 is supported by JIT and enabled in files.
autodoc_type_aliases = {
    "_size_1_t": "int or tuple[int]",
    "_size_2_t": "int or tuple[int, int]",
    "_size_3_t": "int or tuple[int, int, int]",
    "_size_4_t": "int or tuple[int, int, int, int]",
    "_size_5_t": "int or tuple[int, int, int, int, int]",
    "_size_6_t": "int or tuple[int, int, int, int, int, int]",
    "_size_any_opt_t": "int or None or tuple",
    "_size_2_opt_t": "int or None or 2-tuple",
    "_size_3_opt_t": "int or None or 3-tuple",
    "_ratio_2_t": "float or tuple[float, float]",
    "_ratio_3_t": "float or tuple[float, float, float]",
    "_ratio_any_t": "float or tuple",
    "_tensor_list_t": "Tensor or tuple[Tensor]",
}

# Enable overriding of function signatures in the first line of the docstring.
autodoc_docstring_signature = True

# -- katex javascript in header
#
#    def setup(app):
#    app.add_javascript("https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.js")


# -- Options for HTML output ----------------------------------------------
#
# The theme to use for HTML and HTML Help pages.  See the documentation for
# a list of builtin themes.
#
#
#

html_theme = "pytorch_sphinx_theme"
html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]

# Theme options are theme-specific and customize the look and feel of a theme
# further.  For a list of options available for each theme, see the
# documentation.

html_theme_options = {
    "pytorch_project": "docs",
    "canonical_url": "https://pytorch.org/docs/stable/",
    "collapse_navigation": False,
    "display_version": True,
    "logo_only": True,
    "analytics_id": "GTM-T8XT4PS",
}

html_logo = "_static/img/pytorch-logo-dark-unstable.png"
if RELEASE:
    html_logo = "_static/img/pytorch-logo-dark.svg"


# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]

html_css_files = [
    "css/jit.css",
]

from sphinx.ext.coverage import CoverageBuilder


# NB: Due to some duplications of the following modules/functions, we keep
# them as expected failures for the time being instead of return 1
ignore_duplicated_modules = {
    "torch.nn.utils.weight_norm",
    "torch.nn.utils.spectral_norm",
    "torch.nn.parallel.data_parallel",
    "torch.ao.quantization.quantize",
}


def coverage_post_process(app, exception):
    if exception is not None:
        return

    # Only run this test for the coverage build
    if not isinstance(app.builder, CoverageBuilder):
        return

    if not torch.distributed.is_available():
        raise RuntimeError(
            "The coverage tool cannot run with a version "
            "of PyTorch that was built with USE_DISTRIBUTED=0 "
            "as this module's API changes."
        )

    # These are all the modules that have "automodule" in an rst file
    # These modules are the ones for which coverage is checked
    # Here, we make sure that no module is missing from that list
    modules = app.env.domaindata["py"]["modules"]

    # We go through all the torch submodules and make sure they are
    # properly tested
    missing = set()

    def is_not_internal(modname):
        split_name = modname.split(".")
        for name in split_name:
            if name[0] == "_":
                return False
        return True

    # The walk function does not return the top module
    if "torch" not in modules:
        missing.add("torch")

    for _, modname, ispkg in pkgutil.walk_packages(
        path=torch.__path__, prefix=torch.__name__ + "."
    ):
        if is_not_internal(modname):
            if modname not in modules and modname not in ignore_duplicated_modules:
                missing.add(modname)

    output = []

    if missing:
        mods = ", ".join(missing)
        output.append(
            f"\nYou added the following module(s) to the PyTorch namespace '{mods}' "
            "but they have no corresponding entry in a doc .rst file. You should "
            "either make sure that the .rst file that contains the module's documentation "
            "properly contains either '.. automodule:: mod_name' (if you do not want "
            "the paragraph added by the automodule, you can simply use '.. py:module:: mod_name') "
            " or make the module private (by appending an '_' at the beginning of its name)."
        )

    # The output file is hard-coded by the coverage tool
    # Our CI is setup to fail if any line is added to this file
    output_file = path.join(app.outdir, "python.txt")

    if output:
        with open(output_file, "a") as f:
            for o in output:
                f.write(o)


def process_docstring(app, what_, name, obj, options, lines):
    """
    Custom process to transform docstring lines Remove "Ignore" blocks

    Args:
        app (sphinx.application.Sphinx): the Sphinx application object

        what (str):
            the type of the object which the docstring belongs to (one of
            "module", "class", "exception", "function", "method", "attribute")

        name (str): the fully qualified name of the object

        obj: the object itself

        options: the options given to the directive: an object with
            attributes inherited_members, undoc_members, show_inheritance
            and noindex that are true if the flag option of same name was
            given to the auto directive

        lines (List[str]): the lines of the docstring, see above

    References:
        https://www.sphinx-doc.org/en/1.5.1/_modules/sphinx/ext/autodoc.html
        https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html
    """
    import re

    remove_directives = [
        # Remove all xdoctest directives
        re.compile(r"\s*>>>\s*#\s*x?doctest:\s*.*"),
        re.compile(r"\s*>>>\s*#\s*x?doc:\s*.*"),
    ]
    filtered_lines = [
        line for line in lines if not any(pat.match(line) for pat in remove_directives)
    ]
    # Modify the lines inplace
    lines[:] = filtered_lines

    # make sure there is a blank line at the end
    if lines and lines[-1].strip():
        lines.append("")


# Called automatically by Sphinx, making this `conf.py` an "extension".
def setup(app):
    # NOTE: in Sphinx 1.8+ `html_css_files` is an official configuration value
    # and can be moved outside of this function (and the setup(app) function
    # can be deleted).
    html_css_files = [
        "https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css"
    ]

    # In Sphinx 1.8 it was renamed to `add_css_file`, 1.7 and prior it is
    # `add_stylesheet` (deprecated in 1.8).
    add_css = getattr(app, "add_css_file", app.add_stylesheet)
    for css_file in html_css_files:
        add_css(css_file)

    app.connect("build-finished", coverage_post_process)
    app.connect("autodoc-process-docstring", process_docstring)


# From PyTorch 1.5, we now use autogenerated files to document classes and
# functions. This breaks older references since
# https://pytorch.org/docs/stable/torch.html#torch.flip
# moved to
# https://pytorch.org/docs/stable/generated/torch.flip.html
# which breaks older links from blog posts, stack overflow answers and more.
# To mitigate that, we add an id="torch.flip" in an appropriated place
# in torch.html by overriding the visit_reference method of html writers.
# Someday this can be removed, once the old links fade away

from sphinx.writers import html, html5


def replace(Klass):
    old_call = Klass.visit_reference

    def visit_reference(self, node):
        if "refuri" in node and "generated" in node.get("refuri"):
            ref = node.get("refuri")
            ref_anchor = ref.split("#")
            if len(ref_anchor) > 1:
                # Only add the id if the node href and the text match,
                # i.e. the href is "torch.flip#torch.flip" and the content is
                # "torch.flip" or "flip" since that is a signal the node refers
                # to autogenerated content
                anchor = ref_anchor[1]
                txt = node.parent.astext()
                if txt == anchor or txt == anchor.split(".")[-1]:
                    self.body.append(f'<p id="{ref_anchor[1]}"/>')
        return old_call(self, node)

    Klass.visit_reference = visit_reference


replace(html.HTMLTranslator)
replace(html5.HTML5Translator)

# -- Options for HTMLHelp output ------------------------------------------

# Output file base name for HTML help builder.
htmlhelp_basename = "PyTorchdoc"


# -- Options for LaTeX output ---------------------------------------------

latex_elements = {
    # The paper size ('letterpaper' or 'a4paper').
    #
    # 'papersize': 'letterpaper',
    # The font size ('10pt', '11pt' or '12pt').
    #
    # 'pointsize': '10pt',
    # Additional stuff for the LaTeX preamble.
    #
    # 'preamble': '',
    # Latex figure (float) alignment
    #
    # 'figure_align': 'htbp',
}

# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
#  author, documentclass [howto, manual, or own class]).
latex_documents = [
    (
        master_doc,
        "pytorch.tex",
        "PyTorch Documentation",
        "Torch Contributors",
        "manual",
    ),
]


# -- Options for manual page output ---------------------------------------

# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [(master_doc, "PyTorch", "PyTorch Documentation", [author], 1)]


# -- Options for Texinfo output -------------------------------------------

# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
#  dir menu entry, description, category)
texinfo_documents = [
    (
        master_doc,
        "PyTorch",
        "PyTorch Documentation",
        author,
        "PyTorch",
        "One line description of project.",
        "Miscellaneous",
    ),
]


# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {
    "python": ("https://docs.python.org/3", None),
    "numpy": ("https://numpy.org/doc/stable", None),
}

import sphinx.ext.doctest

# -- A patch that prevents Sphinx from cross-referencing ivar tags -------
# See http://stackoverflow.com/a/41184353/3343043
from docutils import nodes
from sphinx import addnodes
from sphinx.util.docfields import TypedField


# Without this, doctest adds any example with a `>>>` as a test
doctest_test_doctest_blocks = ""
doctest_default_flags = sphinx.ext.doctest.doctest.ELLIPSIS
doctest_global_setup = """
import torch
try:
    import torchvision
except ImportError:
    torchvision = None
"""


def patched_make_field(self, types, domain, items, **kw):
    # `kw` catches `env=None` needed for newer sphinx while maintaining
    #  backwards compatibility when passed along further down!

    # type: (List, unicode, Tuple) -> nodes.field
    def handle_item(fieldarg, content):
        par = nodes.paragraph()
        par += addnodes.literal_strong("", fieldarg)  # Patch: this line added
        # par.extend(self.make_xrefs(self.rolename, domain, fieldarg,
        #                           addnodes.literal_strong))
        if fieldarg in types:
            par += nodes.Text(" (")
            # NOTE: using .pop() here to prevent a single type node to be
            # inserted twice into the doctree, which leads to
            # inconsistencies later when references are resolved
            fieldtype = types.pop(fieldarg)
            if len(fieldtype) == 1 and isinstance(fieldtype[0], nodes.Text):
                typename = fieldtype[0].astext()
                builtin_types = ["int", "long", "float", "bool", "type"]
                for builtin_type in builtin_types:
                    pattern = rf"(?<![\w.]){builtin_type}(?![\w.])"
                    repl = f"python:{builtin_type}"
                    typename = re.sub(pattern, repl, typename)
                par.extend(
                    self.make_xrefs(
                        self.typerolename,
                        domain,
                        typename,
                        addnodes.literal_emphasis,
                        **kw,
                    )
                )
            else:
                par += fieldtype
            par += nodes.Text(")")
        par += nodes.Text(" -- ")
        par += content
        return par

    fieldname = nodes.field_name("", self.label)
    if len(items) == 1 and self.can_collapse:
        fieldarg, content = items[0]
        bodynode = handle_item(fieldarg, content)
    else:
        bodynode = self.list_type()
        for fieldarg, content in items:
            bodynode += nodes.list_item("", handle_item(fieldarg, content))
    fieldbody = nodes.field_body("", bodynode)
    return nodes.field("", fieldname, fieldbody)


TypedField.make_field = patched_make_field

copybutton_prompt_text = r">>> |\.\.\. "
copybutton_prompt_is_regexp = True