for (const auto& crops_value : crops_range) {
      int64_t crops_value_int = crops_value.getSExtValue();
      if (crops_value_int < 0)
        return op.emitOpError()
               << "requires all crop values to be nonnegative, but got "
               << crops_attr;

      crops_values.push_back(crops_value_int);
    }
  }

  auto output_type = mlir::cast<TensorType>(op.getOutput().getType());
  if (output_type.hasRank()) {

  %crops = "tf.Const"() {value = dense<[[-1, -2], [-3, -4]]> : tensor<2x2xi32>} : () -> tensor<2x2xi32>
  // expected-error @+1 {{'tf.BatchToSpace' op requires all crop values to be nonnegative, but got dense<[[-1, -2], [-3, -4]]> : tensor<2x2xi32>}}
  %0 = "tf.BatchToSpace"(%arg0, %crops) {block_size = 2 : i64} : (tensor<*xf32>, tensor<2x2xi32>) -> tensor<*xf32>
  func.return
}

// -----

(Div8  n (Const8  [c])) && isNonNegative(n) && isPowerOfTwo8(c)  => (Rsh8Ux64  n (Const64 <typ.UInt64> [log8(c)]))
(Div16 n (Const16 [c])) && isNonNegative(n) && isPowerOfTwo16(c) => (Rsh16Ux64 n (Const64 <typ.UInt64> [log16(c)]))
(Div32 n (Const32 [c])) && isNonNegative(n) && isPowerOfTwo32(c) => (Rsh32Ux64 n (Const64 <typ.UInt64> [log32(c)]))