// particularly seems to be the case for the control edges between _SOURCE
  // and _SINK that the Graph constructor inserts. Copy the input edges and
  // sort the edges, but only the control edges, not data edges!
  // TODO(jmolloy): We should probably just ignore _SOURCE and _SINK nodes.
  // They'll break roundtripping anyway unless we strip them when converting
  // back to graphdef.

<code>package math</code>, which exports function <code>Sin</code>, and
installed the compiled package in the file identified by
<code>"lib/math"</code>.
This table illustrates how <code>Sin</code> is accessed in files
that import the package after the
various types of import declaration.
</p>

<pre class="grammar">
Import declaration          Local name of Sin

import   "lib/math"         math.Sin

  MAXIMUM = 55,
  ARG_MAX = 56,
  MINIMUM = 57,
  LESS = 58,
  NEG = 59,
  PADV2 = 60,
  GREATER = 61,
  GREATER_EQUAL = 62,
  LESS_EQUAL = 63,
  SELECT = 64,
  SLICE = 65,
  SIN = 66,
  TRANSPOSE_CONV = 67,
  SPARSE_TO_DENSE = 68,
  TILE = 69,
  EXPAND_DIMS = 70,
  EQUAL = 71,
  NOT_EQUAL = 72,
  LOG = 73,
  SUM = 74,
  SQRT = 75,
  RSQRT = 76,
  SHAPE = 77,

func.func @_UnaryOpsComposition(%arg0: tensor<4xf32>) -> tensor<4xf32> {

  // CHECK: %[[RESULT0:.*]] = "tf.Asin"(%[[ARG0]])
  // CHECK: %[[RESULT1:.*]] = "tf.Abs"(%[[RESULT0]])
  // CHECK: %[[RESULT2:.*]] = "tf.Log"(%[[RESULT1]])
  // CHECK: return %[[RESULT2]]

  %0 = "tf._UnaryOpsComposition"(%arg0) {op_names = ["Asin", "Abs", "Log"]} : (tensor<4xf32>) -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

^bb0(%arg0: tensor<? x f32>):
  // CHECK: "tfl.sin"(%arg0)
  %0 = "tfl.sin"(%arg0): (tensor<? x f32>) -> tensor<? x f32>
  func.return %0 : tensor<? x f32>
}

// -----

// test invalid Sin input
func.func @testSinWithWrongInputType(tensor<?xi32>) -> tensor<?xi32> {
^bb0(%arg0: tensor<?xi32>):
  // expected-error @+1 {{tfl.sin' op operand #0 must be tensor of 32-bit float values}}

  // CHECK: return [[cst0]], [[cst1]], [[cst2]], [[cst3]], [[cst4]], [[cst5]], [[cst6]]

  %7 = "tfl.abs"(%0) : (tensor<f32>) -> tensor<f32>
  %8 = "tfl.sin"(%1) : (tensor<f32>) -> tensor<f32>
  %9 = "tfl.cos"(%2) : (tensor<f32>) -> tensor<f32>
  %10 = "tfl.log"(%3) : (tensor<f32>) -> tensor<f32>
  %11 = "tfl.sqrt"(%4) : (tensor<f32>) -> tensor<f32>

	"rArr":     "\u21D2",
	"dArr":     "\u21D3",
	"hArr":     "\u21D4",
	"forall":   "\u2200",
	"part":     "\u2202",
	"exist":    "\u2203",
	"empty":    "\u2205",
	"nabla":    "\u2207",
	"isin":     "\u2208",
	"notin":    "\u2209",
	"ni":       "\u220B",
	"prod":     "\u220F",
	"sum":      "\u2211",
	"minus":    "\u2212",
	"lowast":   "\u2217",
	"radic":    "\u221A",

}

def TF_AsinOp : TF_Op<"Asin", [Pure, TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Computes the trignometric inverse sine of x element-wise.";

  let description = [{
The `tf.math.asin` operation returns the inverse of `tf.math.sin`, such that
if `y = tf.math.sin(x)` then, `x = tf.math.asin(y)`.

**Note**: The output of `tf.math.asin` will lie within the invertible range

  func.return %0 : tensor<?xf32>
}

// -----

// CHECK-LABEL: @sin
func.func @sin(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  // CHECK:  mhlo.sine %arg0 : tensor<2xf32>
  %0 = "tf.Sin"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>
}

// -----

// CHECK-LABEL: func @sin_dynamic

  ...