func.func private @add_scatter(%arg0: tensor<*xbf16>, %arg1: tensor<*xbf16>) -> tensor<*xbf16> {
    %0 = "tf.AddV2"(%arg0, %arg1) {device = ""} : (tensor<*xbf16>, tensor<*xbf16>) -> tensor<*xbf16>
    func.return %0 : tensor<*xbf16>
  }

  func.func private @ge_select(%arg0: tensor<*xbf16>, %arg1: tensor<*xbf16>) -> tensor<*xi1> {
    %0 = "tf.GreaterEqual"(%arg0, %arg1) {device = ""} : (tensor<*xbf16>, tensor<*xbf16>) -> tensor<*xi1>

func.func @SparseSoftmaxCrossEntropyWithLogits_with_dynamic(%features: tensor<*xbf16>, %labels: tensor<*xi64>) -> (tensor<2xbf16>, tensor<*xbf16>) {
  // CHECK: tf.SparseSoftmaxCrossEntropyWithLogits
  %0:2 = "tf.SparseSoftmaxCrossEntropyWithLogits"(%features, %labels) : (tensor<*xbf16>, tensor<*xi64>) -> (tensor<2xbf16>, tensor<*xbf16>)
  func.return %0#0, %0#1 : tensor<2xbf16>, tensor<*xbf16>
}

// CHECK-LABEL: func @tanhgrad_float

  }

  func.func private @composite_dequantize_uniform(%arg0: tensor<*xi8>) -> tensor<*xbf16> {
    %cst = "tf.Const"() {value = dense<1.574710e-02> : tensor<bf16>} : () -> tensor<bf16>
    %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<*xi8>) -> tensor<*xbf16>
    %1 = "tf.Mul"(%0, %cst) : (tensor<*xbf16>, tensor<bf16>) -> tensor<*xbf16>
    return %1 : tensor<*xbf16>
  }

// CHECK-LABEL: func @propagate_xladotv2_bf16

  %8 = "tfl.mul"(%2, %3) {fused_activation_function = "NONE"} : (tensor<4xbf16>, tensor<4xbf16>) -> tensor<4xbf16>

  func.return %5, %6, %7, %8 : tensor<bf16>, tensor<4xbf16>, tensor<4xbf16>, tensor<4xbf16>
}

// CHECK-LABEL: @mul_f16
func.func @mul_f16() -> (tensor<f16>, tensor<4xf16>, tensor<4xf16>, tensor<4xf16>) {
  %0 = arith.constant dense<4.5> : tensor<f16>

    %2:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = bf16, value = dense<[4.900000e+01, 8.200000e+02]> : tensor<2xbf16>} : () -> tensor<bf16> loc("const3")
    %3:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = bf16, value = dense<0.000000e+00> : tensor<bf16>} : () -> tensor<bf16> loc("const4")

// CHECK: dtype: DT_HALF
// CHECK: half_val: 15360
// CHECK-NEXT: half_val: 16384
    %2:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = bf16, value = dense<[4.900000e+01, 8.200000e+02]> : tensor<2xbf16>} : () -> tensor<bf16> loc("const3")
// CHECK: name: "const3"
// CHECK-NEXT: op: "Const"
// CHECK: dtype: DT_BFLOAT16
// CHECK: half_val: 16964
// CHECK: half_val: 17485

  EXPECT_THAT(num_bytes, Eq(16));
}

TEST_F(GetSizeInBytesTest, Bfloat16ConstOpSizeInBytes) {
  constexpr absl::string_view kConstOpExpr = R"mlir(
    %cst = "tf.Const"() {value = dense<1.0> : tensor<7xbf16>} : () -> tensor<7xbf16>
  )mlir";

  Block block{};
  TF::ConstOp int_tensor_const_op = ParseConstOp(kConstOpExpr, block, ctx_);

  const int64_t num_bytes = GetSizeInBytes(int_tensor_const_op);

      shape {
        unknown_rank: true
      }
    }
  }
  attr {
    key: "dtype"
    value {
      type: DT_BOOL
    }
  }
}

# CHECK-DAG: tf.IteratorGetNext{{.+}}-> (tensor<1x8xbf16>, tensor<2x?x16xf32>, tensor<*xf64>)
# CHECK-DAG: tf.IteratorGetNextSync{{.+}}-> (tensor<*xi16>, tensor<3x24xi32>, tensor<?x4x32xi64>)

        tensor_shape {
          dim {
            size: 2
          }
        }
        half_val: 16964
        half_val: 17485
        # CHECK: value = dense<[4.900000e+01, 8.200000e+02]> : tensor<2xbf16>
      }
    }
  }
}
node {
  name: "double"
  op: "Const"
  device: "/device:CPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_DOUBLE
    }
  }
  attr {
    key: "value"

  pass_config.legalize_custom_tensor_list_ops =
      toco_flags.legalize_custom_tensor_list_ops();
  // Disable the unfolding of the 16x16 TF::BatchMatMulOp to avoid the
  // conversion to an unsupported 16x16 TFL::FullyConnectedOp.
  if (toco_flags.inference_type() == toco::IODataType::QUANTIZED_INT16) {
    pass_config.unfold_batch_matmul = false;
  }