}

func.func @mean(%arg0: tensor<2x2xf32>, %arg1: tensor<1xi32>) -> tensor<1x2xf32> {
  %0 = "tf.Mean"(%arg0, %arg1) : (tensor<2x2xf32>, tensor<1xi32>) -> tensor<1x2xf32>
  func.return %0 : tensor<1x2xf32>

// CHECK-LABEL: mean
// CHECK:  "tfl.mean"(%arg0, %arg1) <{keep_dims = false}> : (tensor<2x2xf32>, tensor<1xi32>) -> tensor<1x2xf32>
}

perpendicular. And from these Measures I seem to gather this Rule: That the Thickness of the Air is proportional to the Secant of an Angle, whose Sine is a certain mean Proportional between the Sines of Incidence and Refraction. And that mean Proportional, so far as by these Measures I can determine it, is the first of an hundred and six arithmetical mean Proportionals between those Sines counted from the bigger Sine, that is, from the Sine of Refraction when the Refraction is made out of the Glass into the...

      assertThat(cause)
          .hasMessageThat()
          .contains(
              "AsyncFunction.apply returned null instead of a Future. "
                  + "Did you mean to return immediateFuture(null)?");
    }
  }

  @J2ktIncompatible
  @GwtIncompatible // threads
  public void testCatchingAsync_interruptPropagatesToTransformingThread() throws Exception {

      assertThat(cause)
          .hasMessageThat()
          .contains(
              "AsyncFunction.apply returned null instead of a Future. "
                  + "Did you mean to return immediateFuture(null)?");
    }
  }

  @J2ktIncompatible
  @GwtIncompatible // threads
  public void testCatchingAsync_interruptPropagatesToTransformingThread() throws Exception {

  // CHECK: "tfl.mean"(%arg0, %arg1) <{keep_dims = false}>
  %0 = "tfl.mean"(%arg0, %arg1) {keep_dims = false}: (tensor<2x2xf32>, tensor<1xi32>) -> tensor<1x2xf32>
  func.return %0 : tensor<1x2xf32>
}

// -----

// CHECK-LABEL: testMean_true
func.func @testMean_true(%arg0: tensor<2x2xf32>, %arg1 : tensor<1xi32>) -> tensor<1x2xf32> {
  // CHECK: "tfl.mean"(%arg0, %arg1) <{keep_dims = true}>

      "options": {
        "colorMode": "none",
        "graphMode": "area",
        "justifyMode": "auto",
        "orientation": "horizontal",
        "reduceOptions": {
          "calcs": [
            "mean"
          ],
          "fields": "",
          "values": false
        },
        "textMode": "auto"
      },
      "pluginVersion": "10.1.5",
      "targets": [
        {

  if (offset && !IsOfRankedFloatTensorType(offset, 1))
    return op.emitOpError("requires offset to be a 1D float tensor");

  auto mean = GetRankedTensorTypeForOperand(op.getMean());
  if (mean && !IsOfRankedFloatTensorType(mean, 1))
    return op.emitOpError("requires mean to be a 1D float tensor");

  auto variance = GetRankedTensorTypeForOperand(op.getVariance());
  if (variance && !IsOfRankedFloatTensorType(variance, 1))

  let hasOptions = 0;
}

def TFL_MeanOp : TFL_Op<"mean", [
    PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    QuantizableResult,
    Pure]> {
  let summary = "Mean operator";

  let description = [{
    Computes the mean of elements across dimensions of a tensor.
    Reduces input_tensor along the dimensions given in axis.

            filter_tensor, filter_min, filter_max, num_bits=8, narrow_range=True
        )
        bias = array_ops.constant([0.1, 0.2], dtype=dtypes.float32)
        scale, offset = [1.0] * 2, [0.5] * 2
        mean, variance = scale, offset
        out = nn_ops.conv2d(
            q_input,
            q_filter,
            strides=[1, 1, 2, 1],
            dilations=[1, 1, 1, 1],
            padding='SAME',

	totalRx := int64(0)
	connectTime := time.Now()
	for {
		n, err := io.CopyN(xioutil.Discard, r.Body, 128*humanize.KiByte)
		if err != nil && err != io.EOF && err != io.ErrUnexpectedEOF {
			// would mean the network is not stable. Logging here will help in debugging network issues.
			if time.Since(connectTime) < (globalNetPerfMinDuration - time.Second) {
				adminLogIf(ctx, err)
			}
		}
		totalRx += n