ASSERT_THAT(weights_zero_points, SizeIs(out_channel_size));
  ASSERT_THAT(input_tensor->quantization->scale, SizeIs(1));
  ASSERT_THAT(output_tensor->quantization->scale, SizeIs(1));

  const float eps = 1e-7;

  // Bias scale should be input * per_channel_weight_scale.
  for (size_t i = 0; i < out_channel_size; i++) {
    EXPECT_THAT(bias_scales[i], FloatNear(input_tensor->quantization->scale[0] *

// "third_party/tensorflow/compiler/xla/xla_data.pb.h" into
// "third_party/tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc" is
// resolved
LogicalResult PrecheckForXlaConvV2Op(XlaConvV2Op op) {
  auto input_tensor = op.getLhs();
  auto kernel_tensor = op.getRhs();
  auto window_strides = op.getWindowStrides();
  auto padding = op.getPadding();
  auto lhs_dilation = op.getLhsDilation();

    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.
    Unless keepdims is true, the rank of the tensor is reduced by 1 for
    each entry in axis. If keepdims is true, the reduced dimensions are retained
    with length 1.
  }];

  # 2. invert(a) or a = invert(0)
  input_tensor = tf.constant([0, 5, 3, 14], dtype=dtype)
  not_a_and_a, not_a_or_a, not_0 = [bitwise_ops.bitwise_and(
                                      input_tensor, bitwise_ops.invert(input_tensor)),
                                    bitwise_ops.bitwise_or(
                                      input_tensor, bitwise_ops.invert(input_tensor)),