ops::Placeholder(root.WithOpName("input_float"), DT_FLOAT);
  Output input_i64 = ops::Placeholder(root.WithOpName("input_i64"), DT_INT64);

  Output begin_begin =
      ops::Placeholder(root.WithOpName("begin_begin"), DT_INT32);
  Output begin_size = ops::Const(root.WithOpName("begin_size"), {-1});
  Output begin =
      ops::Slice(root.WithOpName("begin"), input_i64, begin_begin, begin_size);

  Output size =

// CHECK: %[[lstm:.*]] = "tfl.unidirectional_sequence_lstm"(%[[input_0]], %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]], %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]],
// CHECK-SAME: %[[input_9]], %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]], %[[input_14]], %[[input_15]], %[[input_16]], %[[input_17]], %[[input_18]], %[[input_19]]

// CHECK-SAME: %[[input_0]],
// CHECK-SAME: %[[input_1]], %[[input_2]], %[[input_3]], %[[input_4]],
// CHECK-SAME: %[[input_5]], %[[input_6]], %[[input_7]], %[[input_8]],
// CHECK-SAME: %[[input_9]], %[[input_9]], %[[input_9]],
// CHECK-SAME: %[[input_10]], %[[input_11]], %[[input_12]], %[[input_13]],
// CHECK-SAME: %[[input_9]], %[[input_9]],
// CHECK-SAME: %[[input_14]], %[[input_15]],
// CHECK-SAME: %[[input_9]], %[[input_9]], %[[input_9]], %[[input_9]]) <{

        %weight = "tf.Const"() {value = dense<1> : tensor<1024x24x24x3xi8>} : () -> tensor<1024x24x24x3xi8>
        %input_i32 = "tf.Cast"(%weight) : (tensor<1024x24x24x3xi8>) -> tensor<1024x24x24x3xi32>
        %output = "tf.Sub"(%input_i32, %zp) : (tensor<1024x24x24x3xi32>, tensor<i32>) -> tensor<1024x24x24x3xi32>
        %cast = "tf.Cast"(%output) : (tensor<1024x24x24x3xi32>) -> tensor<1024x24x24x3xf32>

                    << " inputs but node has " << node->num_inputs();
        }
        return false;
      }

      for (int input_idx = 0, e = input_matchers->size(); input_idx < e;
           input_idx++) {
        if (!MatchAndExplainInput(node, input_idx, listener)) {
          return false;
        }
      }
    }

    std::vector<const Node*> control_deps;

	input16, output16 = output16, input16
	input32, output32 = output32, input32
	input64, output64 = output64, input64
	in := 0
	out := 13
	for i := range input8 {
		input8[i] = u8(in)
		output8[i] = u8(out)
		outputS[i] = u8(out)
		input16[i] = u16(in)
		output16[i] = u16(out)
		input32[i] = u32(in)
		output32[i] = u32(out)
		input64[i] = u64(in)
		output64[i] = u64(out)
		in++
		out++
	}

  %k = "tf_device.launch"() ( {
    %9 = "tf.opK"(%input_0, %input_4, %9) :
      (tensor<i32>, tensor<!tf_type.variant>, tensor<i1>) -> tensor<i32>
    tf_device.return %9 : tensor<i32>
  }) {device = "DEVICE_ALIAS_0"} : () -> tensor<i32>
  %l = "tf_device.launch"() ( {
    %10 = "tf.opL"(%input_1, %input_4, %9) :
      (tensor<f32>, tensor<!tf_type.variant>, tensor<i1>) -> tensor<f32>

    // Use identity op to avoid the weight being constant-folded.
    %identity = "tf.Identity"(%input) : (tensor<*xi8>) -> tensor<*xi8>
    %input_i32 = "tf.Cast"(%identity) : (tensor<*xi8>) -> tensor<*xi32>
    %output = "tf.Sub"(%input_i32, %zp) : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32>
    %cast = "tf.Cast"(%output) : (tensor<*xi32>) -> tensor<*xf32>

  const int input_idx = 0;
  EXPECT_THAT(subgraph->tensors[input_idx]->type, Eq(TensorType_INT8));
  EXPECT_THAT(subgraph->tensors[input_idx]->name, Eq("input"));
  EXPECT_THAT(subgraph->tensors[input_idx]->quantization->scale, SizeIs(1));
  EXPECT_THAT(subgraph->tensors[input_idx]->quantization->zero_point,
              SizeIs(1));
  EXPECT_THAT(subgraph->tensors[input_idx]->quantization->scale[0],

    // the first argument is the input activation.
    int input_idx = 0;
    for (int i = 0; i < args_in_dag->getNumArgs(); i++) {
      if (args_in_dag->getArgName(i)->getAsString() == "\"input\"")
        input_idx = i;
    }
    if (CheckTypeConstraints(args_in_dag->getArg(input_idx), required_types,
                             per_axis)) {