SYS___ATANHL_B                      = 0x848 // 2120
	SYS___CBRTF_B                       = 0x849 // 2121
	SYS___CBRTL_B                       = 0x84A // 2122
	SYS___COPYSIGNF_B                   = 0x84B // 2123
	SYS___COPYSIGNL_B                   = 0x84C // 2124
	SYS___COTANF_B                      = 0x84D // 2125
	SYS___COTAN_B                       = 0x84E // 2126
	SYS___COTANL_B                      = 0x84F // 2127

      },
      "funding": {
        "url": "https://github.com/sponsors/ljharb"
      }
    },
    "node_modules/is-core-module": {
      "version": "2.12.1",
      "resolved": "https://registry.npmjs.org/is-core-module/-/is-core-module-2.12.1.tgz",
      "integrity": "sha512-Q4ZuBAe2FUsKtyQJoQHlvP8OvBERxO3jEmy1I7hcRXcJBGGHFh/aJBswbXuS9sgrDH2QUO8ilkwNPHvHMd8clg==",
      "dev": true,
      "dependencies": {

// points at opBytes{0x83, 00, 0x05,0x81, 00, 0x01, 0x03}) are:
//
//	Yi8, Yml -> Zibo_m, z (0x83, 00)
//	Yi32, Yax -> Zil_, z+2 (0x05)
//	Yi32, Yml -> Zilo_m, z+2+1 (0x81, 0x00)
//	Yrl, Yml -> Zr_m, z+2+1+2 (0x01)
//	Yml, Yrl -> Zm_r, z+2+1+2+1 (0x03)
//
// The Pconstant in the optab line controls the prefix bytes to emit.  That's
// relatively straightforward as this program goes.
//

    Returns:
      A map of: output key -> output result.
    """
    out = nn_ops.conv2d(
        conv_input,
        self.conv_filters,
        strides=[1, 1, 2, 1],
        dilations=[1, 1, 1, 1],
        padding='SAME',
        data_format='NHWC',
    )

    return {'output': out}


# TODO(b/280208261): Add unit tests for comparing unquantized and

DATA ·IEEEConst+2104(SB)/8,$0x0000000064d587de

	/* x^126016 mod p(x), x^125952 mod p(x) */
DATA ·IEEEConst+2112(SB)/8,$0x000000013c4598a8
DATA ·IEEEConst+2120(SB)/8,$0x00000000f4a507b0

	/* x^124992 mod p(x), x^124928 mod p(x) */
DATA ·IEEEConst+2128(SB)/8,$0x00000000b3735430
DATA ·IEEEConst+2136(SB)/8,$0x0000000040e342fc

	/* x^123968 mod p(x), x^123904 mod p(x) */

  %1 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %2 = "tf.Shape"(%arg0) : (tensor<?x1x2x?xf32>) -> tensor<4xi32>
  %3 = "tf.StridedSlice"(%2, %1, %0, %1) {begin_mask = 0 : i64, ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<4xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>
  func.return %3 : tensor<2xi32>

  %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>
  %1 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="VALID", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>

  // expected-error @+1 {{requires orig_input to be rank 4}}
  %result = "tf.MaxPoolGrad"(%orig_input, %orig_output, %grad) {
     data_format = "NHWC",
     ksize = [1, 2, 2, 1],
     padding = "VALID",
     strides = [1, 2, 2, 1]
  } : (tensor<f32>, tensor<10x12x12x64xf32>, tensor<10x12x12x64xf32>) -> tensor<10x24x24x64xf32>
  func.return %result : tensor<10x24x24x64xf32>
}

// -----

func stack2112() { var buf [2112]byte; use(buf[:]); C.callGoStackCheck() }
func stack2116() { var buf [2116]byte; use(buf[:]); C.callGoStackCheck() }
func stack2120() { var buf [2120]byte; use(buf[:]); C.callGoStackCheck() }
func stack2124() { var buf [2124]byte; use(buf[:]); C.callGoStackCheck() }
func stack2128() { var buf [2128]byte; use(buf[:]); C.callGoStackCheck() }

	208: {lang: 0x45, region: 0x136},
	209: {lang: 0x139, region: 0x7c},
	210: {lang: 0x3e9, region: 0x9a},
	212: {lang: 0x3e9, region: 0x9a},
	213: {lang: 0x3fa, region: 0x9a},
	214: {lang: 0x40c, region: 0xb4},
	217: {lang: 0x433, region: 0x9a},
	218: {lang: 0xef, region: 0xc6},
	219: {lang: 0x43e, region: 0x96},
	221: {lang: 0x44d, region: 0x35},
	222: {lang: 0x44e, region: 0x9c},
	226: {lang: 0x45a, region: 0xe8},