}

func.func private @testIf3Then(tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>)
func.func private @testIf3Else(tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>)

// CHECK-LABEL: func @testIf3Result(%arg0: tensor<i1>, %arg1: tensor<*xf32>)
func.func @testIf3Result(tensor<i1>, tensor<*xf32>) -> (tensor<*xf32>, tensor<*xi8>, tensor<*xbf16>) {
^bb0(%arg0: tensor<i1>, %arg1: tensor<*xf32>):
  %1:3 = "tf.If"(%arg0, %arg1) {

      return %0 : tensor<1x?x4xbf16>
    }
  })";

  TF_ASSERT_OK(LegalizeModule(kModuleWithNonConstParam));
}

TEST_F(Tf2XlaRewriterTest, ErrorsWithInvalidNumberOfParametersToArgs) {

  %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>

func.func @single_op_island_forward_block_arg(%arg0: tensor<?x?x?x?xbf16>) -> (tensor<2048xf32>, tensor<?x?x?x?xbf16>) {
  %0:2 = tf_executor.graph {
    %outputs:2, %control = tf_executor.island {
      %1 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2048xf32>} : () -> tensor<2048xf32>
      tf_executor.yield %1, %arg0 : tensor<2048xf32>, tensor<?x?x?x?xbf16>
    }

  %0:2 = tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<2048xf32>} : () -> tensor<2048xf32>
    tf_executor.fetch %outputs, %arg0 : tensor<2048xf32>, tensor<?x?x?x?xbf16>
  }
  return %0#0, %0#1 : tensor<2048xf32>, tensor<?x?x?x?xbf16>
}

  pass_config.unfold_batch_matmul = toco_flags.unfold_batchmatmul();
  pass_config.lower_tensor_list_ops = toco_flags.lower_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;
  }

}

// CHECK-LABEL: func @testEmptybf16
func.func @testEmptybf16() -> (tensor<5xbf16>) {
  %0 = "tf.Const"() { value = dense<5> : tensor<i32> } : () -> tensor<i32>

  // CHECK: [[VAL:%.+]] = "tf.Const"() <{value =  dense<0.000000e+00> : tensor<5xbf16>}>
  // CHECK: return [[VAL]]
  %1 = "tf.Empty"(%0) : (tensor<i32>) -> (tensor<5xbf16>)
  func.return %1 : tensor<5xbf16>
}

// CHECK-LABEL: func @testShapeN

// Float16-DAG: %[[b:.*]] = arith.constant dense<0.000000e+00> : tensor<16xf16>
// Float16-DAG: %[[const:.*]] = "tfl.no_value"() <{value}> : () -> none
// Float16-DAG: %[[dq_w:.*]] = "tfl.dequantize"(%[[w]]) : (tensor<3x3x3x8x16xf16>) -> tensor<3x3x3x8x16xf32>
// Float16-DAG: %[[dq_b:.*]] = "tfl.dequantize"(%[[b]]) : (tensor<16xf16>) -> tensor<16xf32>

	}
	origPtr := uint(br.ReadBits(24))

	// If not every byte value is used in the block (i.e., it's text) then
	// the symbol set is reduced. The symbols used are stored as a
	// two-level, 16x16 bitmap.
	symbolRangeUsedBitmap := br.ReadBits(16)
	symbolPresent := make([]bool, 256)
	numSymbols := 0
	for symRange := uint(0); symRange < 16; symRange++ {
		if symbolRangeUsedBitmap&(1<<(15-symRange)) != 0 {

				http.Redirect(w, r, u.String(), http.StatusTemporaryRedirect)
				return
			}
		}
		h.ServeHTTP(w, r)
	})
}

var redirectPrefixes = map[string]struct{}{
	"favicon-16x16.png": {},
	"favicon-32x32.png": {},
	"favicon-96x96.png": {},
	"index.html":        {},
	minioReservedBucket: {},
}

// Fetch redirect location if urlPath satisfies certain