if (mlir::isa<NoneType>(operand.getType()))
        operands.push_back(kTfLiteOptionalTensor);
      else if (auto stats_op =
                   llvm::dyn_cast_or_null<mlir::quantfork::StatisticsOp>(
                       operand.getDefiningOp()))
        operands.push_back(tensor_index_map.lookup(stats_op.getArg()));
      else
        operands.push_back(tensor_index_map.lookup(operand));
    }

class TFL_OperandsHaveSameDimsTrait<int x, int y, int i, int j> :
  PredOpTrait<"dim " # i # " of operand " # x # " equals to dim " # j #
    " of operand " # y,
    TFL_OperandsHaveSameDims<x, y, i, j>>;

// Return true if number of elements of x-th operand is the same as j-th dim of
// y-th operand or any of those operands does not have static shape.
class TFL_NumElementsEqualsDim<int x, int y, int j> :

  // Forcely assign operand types to result types (the i-th operand type will
  // assign to i-th result type). Returns true if anything is changed.
  bool ForceTypeForPassThroughOperands(Operation* op, OperandRange operands,
                                       ResultRange results);

  // Makes result types match the operand types (the i-th result type will
  // match the i-th operand type). Returns true if anything is changed.

  // expected-error @+1 {{op failed to verify that operand 0 is 0-D}}
  %0 = "tfl.range"(%arg0, %arg1, %arg2) : (tensor<1xi32>, tensor<i32>, tensor<i32>) -> tensor<?xi32>
  func.return %0 : tensor<?xi32>
}

// -----

func.func @testRangeOutputTypeMismatch(%arg0 : tensor<i32>, %arg1 : tensor<i32>, %arg2 : tensor<i32>) -> tensor<?xf32> {
  // expected-error @+1 {{op failed to verify that operands and output must have same element type}}

}

/* VA-form 3-register + SHB operands */
func AOP_IRRR(op uint32, d uint32, a uint32, b uint32, simm uint32) uint32 {
	return op | (d&31)<<21 | (a&31)<<16 | (b&31)<<11 | (simm&0xF)<<6
}

/* VX-form 1-register + SIM operands */
func AOP_IR(op uint32, d uint32, simm uint32) uint32 {
	return op | (d&31)<<21 | (simm&31)<<16
}

/* XX1-form 3-register operands, 1 VSR operand */

	}

	tests := []struct {
		name       string
		operations []operation
		operands   []*framework.QueuedPodInfo
		expected   []*framework.QueuedPodInfo
	}{
		{
			name: "add two pod to activeQ and sort them by the timestamp",
			operations: []operation{
				addPodActiveQ,
				addPodActiveQ,
			},
			operands: []*framework.QueuedPodInfo{pInfo2, pInfo1},

// ACRC32, and p.From and p.To as operands (obj.Addr).  The linker scans optab
// to find the entry with the given p.As and then looks through the ytable for
// that instruction (the second field in the optab struct) for a line whose
// first two values match the Ytypes of the p.From and p.To operands.  The
// function oclass computes the specific Ytype of an operand and then the set

)

const (
	REGFROM = 1
)

type Optab struct {
	as    obj.As
	a1    uint8 // Prog.From
	a2    uint8 // 2nd source operand, Prog.Reg or Prog.RestArgs[XXX]
	a3    uint8 // 3rd source operand, Prog.RestArgs[XXX]
	a4    uint8 // Prog.To
	a5    uint8 // 2nd destination operand, Prog.RegTo2 or Prog.RestArgs[XXX]
	type_ int8
	size_ int8 // the value of this field is not static, use the size() method to return the value

// CHECK-LABEL: dot_general_upstream_srq_wrong_contracting
// CHECK: stablehlo.dot_general
// CHECK-NOT: tfl.batch_matmul

// -----

// Tests static range quantized dot_general with float operands

// CHECK-LABEL: dot_general_upstream_srq_float_operands
func.func @dot_general_upstream_srq_float_operands(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1x2x4x5xf32>) -> tensor<1x2x3x5xf32> {

  let description = [{
    This pass outlines the body of a `tf_device.cluster` into a function and
    replaces the `tf_device.cluster` op with an equivalent `tf_device.cluster_func`
    op. Implicit operands will be captured and materialized as explicit arguments to
    the newly created functions and associated `tf_device.cluster_func` ops.

    For example, the following:

    ```mlir