return klass(val.cast(val.type.target()))
		except Exception:
			pass
	return matcher

goobjfile.pretty_printers.extend([makematcher(var) for var in vars().values() if hasattr(var, 'pattern')])
#
#  Utilities
#

def pc_to_int(pc):
	# python2 will not cast pc (type void*) to an int cleanly
	# instead python2 and python3 work with the hex string representation

      }
    }

    return false;
  }

  // Checks if op calls a composite function and all the inputs are quantized.
  bool IsCompositeFunction(TF::PartitionedCallOp call_op) const {
    if (!call_op->hasAttr(kQuantTraitAttrName)) {
      return false;
    }

    const auto f_attr = call_op.getFAttr().dyn_cast<FlatSymbolRefAttr>();
    if (!f_attr || !f_attr.getValue().starts_with("composite_")) {
      return false;

b/,builtin:/\b(?:__import__|abs|all|any|apply|ascii|basestring|bin|bool|buffer|bytearray|bytes|callable|chr|classmethod|cmp|coerce|compile|complex|delattr|dict|dir|divmod|enumerate|eval|execfile|file|filter|float|format|frozenset|getattr|globals|hasattr|hash|help|hex|id|input|int|intern|isinstance|issubclass|iter|len|list|locals|long|map|max|memoryview|min|next|object|oct|open|ord|pow|property|range|raw_input|reduce|reload|repr|reversed|round|set|setattr|slice|sorted|staticmethod|str|sum|super|t...

                                     const char* attr_name, TF_Status* status) {
  auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelConstruction*>(ctx);
  return cc_ctx->HasAttr(attr_name);
}

TF_StringView TF_OpKernelConstruction_GetName(TF_OpKernelConstruction* ctx) {
  auto* cc_ctx = reinterpret_cast<tensorflow::OpKernelConstruction*>(ctx);
  TF_StringView string_view_of_name;

  def lookup(self, f_name, func_def=None, optional=False):
    if f_name in self._op_defs:
      return self._op_defs[f_name]

    if isinstance(func_def, types.FunctionType):
      if not hasattr(func_def, '_tfr_op_name'):
        # skip a non-composition function
        if optional:
          return (None, None)
        else:
          raise KeyError('OpDef does not exist: ' + f_name)

    };
    return llvm::all_of(op->getOperandTypes(), is_not_variant) &&
           llvm::all_of(op->getResultTypes(), is_not_variant);
  };

  auto is_set_item_legal = [](Operation *op) {
    return op->hasAttr("resize_if_index_out_of_bounds") &&
           op->getAttr("resize_if_index_out_of_bounds")
               .cast<mlir::BoolAttr>()
               .getValue();
  };

  ConversionTarget target(*context);

    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    is_coroutine: bool = True,
) -> Any:
    if field:
        errors = []
        if not hasattr(field, "serialize"):
            # pydantic v1
            response_content = _prepare_response_content(
                response_content,
                exclude_unset=exclude_unset,

  return changed;
}

bool ShapeInference::InferShapeForFunctionAttachedToXlaHostCompute(
    XlaHostComputeOp op) {
  const std::string kShapeInferenceGraph = "shape_inference_graph";
  if (!op->hasAttr(kShapeInferenceGraph)) {
    return false;
  }

  ModuleOp module = op->getParentOfType<ModuleOp>();
  func::FuncOp func = module.lookupSymbol<func::FuncOp>(

    if (hashIndex >= 0) {
      hashStr = url.substring(hashIndex);
      buf.push("&hash=");
      buf.push(encodeURIComponent(hashStr));
      buf.push(hashStr);
    }
  });

  $result.on("click", "a.favorite", function(e) {
    var $favorite = $(this),
        values = $favorite.attr("href").split("#"),
        actionUrl,
        docId;
    if (values.length === 2 && $queryId.length > 0) {
      docId = values[1];

  // attribute. This provides a hook for the user to represent the variable
  // tensor in the MLIR level.
  if (auto* inst = value.getDefiningOp();
      inst && inst->hasAttr("tfl.is_variable")) {
    is_variable = true;
  }

  bool has_rank = type.hasRank();

  if (shape_signature.empty()) {
    return tflite::CreateTensor(