// the shape type is static, 1D and has less-than-equal to 4 elements.
    bool is_output_shape_dynamic =
        (!output_type.hasRank() || (output_type.getRank() > 4) ||
         (output_type.getNumDynamicDims() > 0));
    bool is_broadcast_shape_dynamic =
        (!shape_type.hasStaticShape() || (shape_type.getRank() != 1) ||
         (shape_type.getDimSize(0) > 4));

// Returns True if the operand type is RankedTensorType and valid.
def HasValidRankedTensor : Constraint<CPred<
  "$0.getType().isa<RankedTensorType>() && "
  "$0.getType().cast<RankedTensorType>().getNumDynamicDims() <= 1">>;

// Check if the truncated shape of the lhs is equal to the shape of rhs
def IsPrefixTruncatedShapeEqualTo : Constraint<CPred<
  "GetShape($0, true) == GetShape($1)">>;

    auto end_indices_type = mlir::cast<RankedTensorType>(
        real_dynamic_slice_op.getLimitIndices().getType());

    if (start_indices_type.getNumDynamicDims() != 0 ||
        end_indices_type.getNumDynamicDims() != 0) {
      return rewriter.notifyMatchFailure(
          real_dynamic_slice_op,
          "Start indices and limit indices must not have dynamic dimensions.");
    }

      *begin != 0 || *end != 1 || *strides != 1)
    return {};

  // First tensor dimension is dynamic.
  auto arg_ty = tensor.getType().dyn_cast<ShapedType>();
  if (!arg_ty || !arg_ty.hasRank() || arg_ty.getNumDynamicDims() != 1 ||
      !arg_ty.isDynamicDim(0))
    return {};

  // Argument tensor rank is equal to the number of packed dimensions.
  if (arg_ty.getRank() != getValues().size()) return {};