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dow","this","noGlobal","arr","getProto","Object","getPrototypeOf","slice","flat","array","call","concat","apply","push","indexOf","class2type","toString","hasOwn","hasOwnProperty","fnToString","ObjectFunctionString","support","isFunction","obj","nodeType","item","isWindow","preservedScriptAttributes","type","src","nonce","noModule","DOMEval","code","node","doc","i","val","script","createElement","text","getAttribute","setAttribute","head","appendChild","parentNode","removeChild","toType","versio...

pkg text/template/parse, const NodeComment NodeType
pkg text/template/parse, const ParseComments = 1
pkg text/template/parse, const ParseComments Mode
pkg text/template/parse, method (*CommentNode) Copy() Node
pkg text/template/parse, method (*CommentNode) String() string
pkg text/template/parse, method (CommentNode) Position() Pos
pkg text/template/parse, method (CommentNode) Type() NodeType
pkg text/template/parse, type CommentNode struct

	case parse.NodeAction:
		out.Merge(getParams(n.(*parse.ActionNode).Pipe))
	case parse.NodeCommand:
		for _, arg := range n.(*parse.CommandNode).Args {
			out.Merge(getParams(arg))
		}
	case parse.NodePipe:
		for _, c := range n.(*parse.PipeNode).Cmds {
			out.Merge(getParams(c))
		}
	case parse.NodeList:
		for _, next := range n.(*parse.ListNode).Nodes {
			out.Merge(getParams(next))
		}
	}
	return out

      SmallVector<Value, 4> inputs;
      inputs.reserve(candidate_op->getNumOperands());
      for (auto operand : candidate_op->getOperands()) {
        Type operand_type = operand.getType();
        if (mlir::isa<NoneType>(operand_type)) {
          inputs.push_back(operand);
          continue;
        }

        auto ele_type =
            mlir::cast<TensorType>(operand.getType()).getElementType();
        if (auto dq_op =

      # TODO(fengliuai): use node_type once it
      if node_type == TFRTypes.SHAPE:
        print('TODO: use "node_type"')
      if node.attr == 'shape' and tensor_type == TFRTypes.TENSOR:
        ssa_value = self._ssa_name('shape')
        self._emit_with_loc(
            '\n{} = tfr.get_shape {} -> !shape.shape'.format(ssa_value, value),
            node)
        return (ssa_value, TFRTypes.SHAPE)

    return failure();
  }
  lstm_variant->use_projection =
      !mlir::isa<NoneType>(op.getProjectionWeights().getType());
  lstm_variant->use_peephole =
      !mlir::isa<NoneType>(op.getCellToOutputWeights().getType());
  lstm_variant->use_layer_norm =
      !mlir::isa<NoneType>(op.getForgetLayerNormCoefficients().getType());

  *op_property = operator_property::GetOperatorProperty(

          TypeIsPred<"input_gate_bias", NoneType>,
          TypeIsPred<"input_layer_norm_coefficients", NoneType>]>,
     Neg<Or<[
       TypeIsPred<"input_to_input_weights", NoneType>,
       TypeIsPred<"recurrent_to_input_weights", NoneType>,
       TypeIsPred<"input_gate_bias", NoneType>]>>]>>;


// TODO(b/137798843): Need to add an additional constraint for both LSTM and

// arguments specifying the input, weight, projection and bias.
// The weight, projection, bias and layer norm scale all need to be
// RankedTensorType.
// This class sets the layer norm coefficients to NoneType.
class ConvertLSTMCellSimpleToFusedLSTM {
 public:
  explicit ConvertLSTMCellSimpleToFusedLSTM(mlir::func::FuncOp fused_func_op)
      : fused_func_op_(fused_func_op),
        couple_input_forget_gates_(false),

		return nil, nil
	}

	// Call runFunc for each Func{Decl,Lit}.
	inspect := pass.ResultOf[inspect.Analyzer].(*inspector.Inspector)
	nodeTypes := []ast.Node{
		(*ast.FuncLit)(nil),
		(*ast.FuncDecl)(nil),
	}
	inspect.Preorder(nodeTypes, func(n ast.Node) {
		runFunc(pass, n)
	})
	return nil, nil
}

func runFunc(pass *analysis.Pass, node ast.Node) {
	// Find scope of function node