}
}

func TestMicroTimeUnmarshalJSONAndProtoEqual(t *testing.T) {
	cases := []struct {
		name   string
		input  MicroTime
		result bool
	}{
		{"nanosecond level precision", UnixMicro(123, 123123123), true},
		{"microsecond level precision", UnixMicro(123, 123123000), true},
	}

	for _, c := range cases {
		t.Run(c.name, func(t *testing.T) {
			jsonData, err := c.input.MarshalJSON()
			if err != nil {

	Write(b []byte) (n int, err error)
	// Width returns the value of the width option and whether it has been set.
	Width() (wid int, ok bool)
	// Precision returns the value of the precision option and whether it has been set.
	Precision() (prec int, ok bool)

	// Flag reports whether the flag c, a character, has been set.
	Flag(c int) bool
}

// Formatter is implemented by any value that has a Format method.

  }

  /**
   * Returns statistics over a dataset containing the given values.
   *
   * @param values a series of values, which will be converted to {@code double} values (this may
   *     cause loss of precision)
   */
  public static Stats of(Iterable<? extends Number> values) {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.addAll(values);
    return accumulator.snapshot();
  }

  module {
    func.func @main(%arg0: tensor<1x1024xf32>, %arg1: tensor<1024x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} {
      %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0], precision = [] : (tensor<1x1024xf32>, tensor<1024x3xf32>) -> tensor<1x3xf32>
      return %0 : tensor<1x3xf32>
    }
  }
)mlir";

constexpr absl::string_view kModuleDynamic = R"mlir(
  module {

  }

  /**
   * Returns statistics over a dataset containing the given values.
   *
   * @param values a series of values, which will be converted to {@code double} values (this may
   *     cause loss of precision)
   */
  public static Stats of(Iterable<? extends Number> values) {
    StatsAccumulator accumulator = new StatsAccumulator();
    accumulator.addAll(values);
    return accumulator.snapshot();
  }

  /**

    return (long) z;
  }

  private static final double MIN_LONG_AS_DOUBLE = -0x1p63;
  /*
   * We cannot store Long.MAX_VALUE as a double without losing precision. Instead, we store
   * Long.MAX_VALUE + 1 == -Long.MIN_VALUE, and then offset all comparisons by 1.
   */
  private static final double MAX_LONG_AS_DOUBLE_PLUS_ONE = 0x1p63;

  /**

    return (long) z;
  }

  private static final double MIN_LONG_AS_DOUBLE = -0x1p63;
  /*
   * We cannot store Long.MAX_VALUE as a double without losing precision. Instead, we store
   * Long.MAX_VALUE + 1 == -Long.MIN_VALUE, and then offset all comparisons by 1.
   */
  private static final double MAX_LONG_AS_DOUBLE_PLUS_ONE = 0x1p63;

  /**

    %0 = stablehlo.constant dense<0.000000e+00> : tensor<f32>
    %1 = stablehlo.constant dense<6.000000e+00> : tensor<f32>
    %2 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<?x2xf32>, tensor<2x2xf32>) -> tensor<?x2xf32>
    %3 = shape.shape_of %2 : tensor<?x2xf32> -> tensor<2xindex>

    %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<1x1024xf32>, tensor<1024x3xf32>) -> tensor<1x3xf32>
    %1 = stablehlo.add %0, %arg2 : tensor<1x3xf32>
    return %0 : tensor<1x3xf32>
  }

  // CHECK: @composite_dot_general_with_relu_fn_1

      %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<1x1024xf32>, tensor<1024x3xf32>) -> tensor<1x3xf32>
      return %0 : tensor<1x3xf32>
    }
  }
)mlir";

TEST_F(LiftAsFunctionCallTest, LiftedFunctionSucceeds) {