}
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 1
      }
    }
  }
}
node {
  name: "Const_1"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {

  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
          }
        }
      }
    }
  }
}
node {
  name: "ParseExample/Const_1"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {

	for {
		x := v_0
		s := v_1
		v.reset(OpInt64Make)
		v0 := b.NewValue0(v.Pos, OpRsh32x32, typ.UInt32)

    //        |         |
    //        |         |
    //      Const_0    Const_1
    //
    const float const0_val[] = {1.0, 2.0, 3.0, 4.0};
    const float const1_val[] = {1.0, 0.0, 0.0, 1.0};
    TF_Operation* const0 = FloatConst2x2(graph_, s_, const0_val, "Const_0");
    TF_Operation* const1 = FloatConst2x2(graph_, s_, const1_val, "Const_1");
    TF_Operation* matmul = MatMul(graph_, s_, const0, const1, "MatMul");

  %2 = stablehlo.add %1, %0 : tensor<1x4x3x3xf32>
  return %2 : tensor<1x4x3x3xf32>
}
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x3x3x4xf32>) -> tensor<1x4x3x3xf32>
// CHECK-DAG: %[[CONST_0:.+]] = stablehlo.constant
// CHECK-DAG: %[[TRANSPOSE_0:.+]] = stablehlo.transpose %[[CONST_0]], dims = [0, 2, 3, 1] : (tensor<1x4x3x3xf32>) -> tensor<1x3x3x4xf32>

// Check that the shape of the add is changed to reflect the deferred transpose.

  Output const_0d = ops::Const(root.WithOpName("const_0d"), 42);

  Output const_2d = ops::Const(root.WithOpName("const_2d"), {{1, 2}, {4, 3}});

  EXPECT_THAT(const_0d.node(), NodeWith(ConstantValue(42)));
  EXPECT_THAT(const_0d.node(), NodeWith(ConstantValue(42), Name("const_0d")));

  EXPECT_THAT(const_2d.node(), NodeWith(ConstantValue({{1, 2}, {4, 3}})));

// CHECK: dtype: DT_HALF
// CHECK: half_val: 15360
    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<[1.0, 2.0]> : tensor<2xf16>} : () -> tensor<2xf16> loc("const2")
// CHECK: name: "const2"
// CHECK-NEXT: op: "Const"
// CHECK: dtype: DT_HALF
// CHECK: half_val: 15360
// CHECK-NEXT: half_val: 16384

//   |        |          |
//   |     Const_3       |
//   |                   |
//   |        ^          |
//   |       z|          |     (MatMul Forward Graph)
//   |        |          |
//   |      MatMul_0     |
//   |     /        \    |
//   |    ^          ^   |
//   |    |          |   |
//   |---x|         y|---|
//        |          |
//        |          |
//      Const_0   Const_1
//

	MUL	acc0, hlp1, hlp0

	MUL	const0, hlp1, t0
	ADDS	t0, acc0, acc0
	UMULH	const0, hlp0, t1

	MUL	const1, hlp0, t0
	ADCS	t0, acc1, acc1
	UMULH	const1, hlp0, y0

	MUL	const2, hlp0, t0
	ADCS	t0, acc2, acc2
	UMULH	const2, hlp0, acc0

	MUL	const3, hlp0, t0
	ADCS	t0, acc3, acc3

	UMULH	const3, hlp0, hlp0
	ADC	$0, hlp0

	ADDS	t1, acc1, acc1
	ADCS	y0, acc2, acc2
	ADCS	acc0, acc3, acc3

        }
        string_val: ""
      }
    }
  }
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
        }
      }
    }
  }
}
node {
  name: "Const2"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT64
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT64
        tensor_shape {