// CHECK-NEXT: %[[ZEROS_LIKE:.*]] = "tf.ZerosLike"(%[[ADDN]]) : (tensor<10xf32>) -> tensor<10xf32>
  %zeros-like = "tf.ZerosLike"(%addn) : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  // CHECK-NEXT: %[[ADDN2:.*]] = "tf.AddN"(%[[ADDN]], %[[ZEROS_LIKE]]) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>

  }

  const std::string type_index_name =
      ::tensorflow::port::MaybeAbiDemangle(input.TypeId().name());

  return ::tensorflow::errors::Internal(
      "No unary variant unary_op function found for op ZEROS_LIKE Variant "
      "type_name: ",
      type_index_name, " for device type: ", cc_ctx->device()->name());
}

void TF_ZerosLikeVariant(TF_OpKernelContext* ctx,

  PACK = 83,
  LOGICAL_OR = 84,
  ONE_HOT = 85,
  LOGICAL_AND = 86,
  LOGICAL_NOT = 87,
  UNPACK = 88,
  REDUCE_MIN = 89,
  FLOOR_DIV = 90,
  REDUCE_ANY = 91,
  SQUARE = 92,
  ZEROS_LIKE = 93,
  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,

  PACK = 83,
  LOGICAL_OR = 84,
  ONE_HOT = 85,
  LOGICAL_AND = 86,
  LOGICAL_NOT = 87,
  UNPACK = 88,
  REDUCE_MIN = 89,
  FLOOR_DIV = 90,
  REDUCE_ANY = 91,
  SQUARE = 92,
  ZEROS_LIKE = 93,
  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,

  PACK = 83,
  LOGICAL_OR = 84,
  ONE_HOT = 85,
  LOGICAL_AND = 86,
  LOGICAL_NOT = 87,
  UNPACK = 88,
  REDUCE_MIN = 89,
  FLOOR_DIV = 90,
  REDUCE_ANY = 91,
  SQUARE = 92,
  ZEROS_LIKE = 93,
  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,

      // Note: Needs to be zeros rather than ones since there's currently no
      // ones_like for variants.
      aid = output_tensor.ZerosLike();
    } else {
      // TODO(allenl): Figure out why using zeros_like everywhere causes issues
      // for some gradient functions and if there's another way to work around
      // it (e.g. conds instead of ifs). The value shouldn't really matter.

int64_t ToId(const AbstractTensorHandle* t) {
  return static_cast<int64_t>(reinterpret_cast<uintptr_t>(t));
}

Status ZerosLike(AbstractContext* ctx, AbstractTensorHandle* t,
                 AbstractTensorHandle** result) {
  AbstractOperationPtr op(ctx->CreateOperation());
  TF_RETURN_IF_ERROR(op->Reset("ZerosLike", /*raw_device_name=*/nullptr));
  if (isa<tracing::TracingOperation>(op.get())) {

      // 'NoGradient'. Therefore, we need to add 'ZerosLike' nodes for each
      // 'NoGradient' output before we call the gradient function for 'n'.
      // TODO(andydavis) If static shapes are known, replace 'ZerosLike' with
      // zero-filled Constant node of appropriate shape.
      for (const int dy_index : no_grad_dy_indices) {
        dy[dy_index] = ops::ZerosLike(scope_, Output(n, dy_index));
      }
    }

       $min, $max, $bits, $narrow_range),
      (TF_FakeQuantWithMinMaxVarsOp $input,
         (TF_ConstOp $min), (TF_ConstOp $max), $bits, $narrow_range)>;

// ZerosLike op patterns.
//===----------------------------------------------------------------------===//


class LowerInitializationOp<Op FromOp, int initial_val>
  : Pat<(FromOp:$src_op

      // The accumulation zero point is 0.
      %accum_scale = "tf.Mul"(%input_scale, %filter_scale) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
      %accum_zp_f32 = "tf.ZerosLike"(%accum_scale) : (tensor<*xf32>) -> tensor<*xf32>
      %accum_zp = "tf.Cast"(%accum_zp_f32) {Truncate = false} : (tensor<*xf32>) -> tensor<*xi32>
      %main_out = "tf.PartitionedCall"(%input, %filter, %input_scale, %input_zp,