MAP_NORESERVE                 = 0x40
	MAP_PRIVATE                   = 0x2
	MAP_RENAME                    = 0x20
	MAP_SHARED                    = 0x1
	MAP_TEXT                      = 0x400
	MAP_TYPE                      = 0xf
	MCL_CURRENT                   = 0x1
	MCL_FUTURE                    = 0x2
	MSG_CTRUNC                    = 0x10
	MSG_DONTROUTE                 = 0x4
	MSG_DONTWAIT                  = 0x80

	MAP_NORESERVE                 = 0x40
	MAP_PRIVATE                   = 0x2
	MAP_RENAME                    = 0x20
	MAP_SHARED                    = 0x1
	MAP_TEXT                      = 0x400
	MAP_TYPE                      = 0xf
	MCAST_BLOCK_SOURCE            = 0x2b
	MCAST_EXCLUDE                 = 0x2
	MCAST_INCLUDE                 = 0x1
	MCAST_JOIN_GROUP              = 0x29
	MCAST_JOIN_SOURCE_GROUP       = 0x2d

from starlette.routing import BaseRoute
from starlette.types import ASGIApp, Lifespan, Receive, Scope, Send
from typing_extensions import Annotated, Doc, deprecated

AppType = TypeVar("AppType", bound="FastAPI")


class FastAPI(Starlette):
    """
    `FastAPI` app class, the main entrypoint to use FastAPI.

    Read more in the

    // activation shape needs to be static to convert negative indices in
    // TensorFlow to absolute indices required by HLO.
    RankedTensorType act_type = mlir::dyn_cast<RankedTensorType>(act.getType());
    if (!act_type) return failure();
    Type act_ele_type = act_type.getElementType();
    // To support mixed precision, the statistics type, which maybe more
    // precise than the input types, are used for this op.

LogicalResult TPUExecuteAndUpdateVariablesOp::verify() {
  TPUExecuteAndUpdateVariablesOp op = *this;
  int num_resource_args = 0;
  for (Type arg_type : op.getArgs().getTypes())
    if (arg_type.cast<TensorType>().getElementType().isa<ResourceType>())
      ++num_resource_args;

  auto check_attr = [&](ArrayAttr indices, llvm::StringRef name,

	MAP_HUGE_SHIFT                              = 0x1a
	MAP_PRIVATE                                 = 0x2
	MAP_SHARED                                  = 0x1
	MAP_SHARED_VALIDATE                         = 0x3
	MAP_TYPE                                    = 0xf
	MCAST_BLOCK_SOURCE                          = 0x2b
	MCAST_EXCLUDE                               = 0x0
	MCAST_INCLUDE                               = 0x1

	Size               uint32
	Config             uint64
	Sample             uint64
	Sample_type        uint64
	Read_format        uint64
	Bits               uint64
	Wakeup             uint32
	Bp_type            uint32
	Ext1               uint64
	Ext2               uint64
	Branch_sample_type uint64
	Sample_regs_user   uint64
	Sample_stack_user  uint32
	Clockid            int32

  ::flatbuffers::uoffset_t start_;
  void add_shape(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> shape) {
    fbb_.AddOffset(VariantSubType::VT_SHAPE, shape);
  }
  void add_type(tflite::TensorType type) {
    fbb_.AddElement<int8_t>(VariantSubType::VT_TYPE, static_cast<int8_t>(type), 0);
  }
  void add_has_rank(bool has_rank) {

		{"MAP_RENAME", Const, 0},
		{"MAP_RESERVED0080", Const, 0},
		{"MAP_RESERVED0100", Const, 1},
		{"MAP_SHARED", Const, 0},
		{"MAP_STACK", Const, 0},
		{"MAP_TRYFIXED", Const, 3},
		{"MAP_TYPE", Const, 0},
		{"MAP_WIRED", Const, 3},
		{"MAXIMUM_REPARSE_DATA_BUFFER_SIZE", Const, 4},
		{"MAXLEN_IFDESCR", Const, 0},
		{"MAXLEN_PHYSADDR", Const, 0},
		{"MAX_ADAPTER_ADDRESS_LENGTH", Const, 0},

lues();};CodeMap.prototype.getAllLibrariesEntries=function(){return this.libraries_.exportValues();};CodeMap.CodeState={COMPILED:0,OPTIMIZABLE:1,OPTIMIZED:2};CodeMap.CodeEntry=function(size,opt_name,opt_type){this.id=tr.b.GUID.allocateSimple();this.size=size;this.name_=opt_name||'';this.type=opt_type||'';this.nameUpdated_=false;};CodeMap.CodeEntry.prototype={__proto__:Object.prototype,get name(){return this.name_;},set name(value){this.name_=value;},toString(){this.name_+': '+this.size.toString(...