// 'f' (-ddd.dddd, no exponent),
// 'g' ('e' for large exponents, 'f' otherwise),
// 'G' ('E' for large exponents, 'f' otherwise),
// 'x' (-0xd.ddddp±ddd, a hexadecimal fraction and binary exponent), or
// 'X' (-0Xd.ddddP±ddd, a hexadecimal fraction and binary exponent).
//
// The precision prec controls the number of digits (excluding the exponent)
// printed by the 'e', 'E', 'f', 'g', 'G', 'x', and 'X' formats.

	intVal     struct{ val *big.Int }   // Int values not representable as an int64
	ratVal     struct{ val *big.Rat }   // Float values representable as a fraction
	floatVal   struct{ val *big.Float } // Float values not representable as a fraction
	complexVal struct{ re, im Value }
)

func (unknownVal) Kind() Kind { return Unknown }
func (boolVal) Kind() Kind    { return Bool }

    that the device attribute exists.
  }];

  let constructor = "tensorflow::tf2xla::internal::CreateVerifyClusteringPass()";
}

def TPUClusterFormationPass : Pass<"tf-tpu-cluster-formation", "ModuleOp"> {
  let summary = "Forms clusters from operations assigned to the same TPU computation";

  let description = [{
    TPU computations from the frontend are composed of a `tf.TPUReplicateMetadata`

   * the data points and the best fit line). For this fit, this error is a fraction {@code sqrt(1 -
   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a

//     sign_bit exponent_bits fraction_bits
//
//   Here, sign_bit is a single bit that designates the sign of the
//   number.
//
//   For float, there are 8 exponent bits and 23 fraction bits.
//
//   For double, there are 11 exponent bits and 52 fraction bits.
//
//   More details can be found at
//   http://en.wikipedia.org/wiki/IEEE_floating-point_standard.
//
// Template parameter:
//

            withKotlinDslPlugin().prependText("import java.net.URL").appendText(
                """
                dependencies { implementation("test:external-plugins") }

                abstract class ConcurrentWork : WorkAction<WorkParameters.None> {
                    private val logger: org.slf4j.Logger = org.slf4j.LoggerFactory.getLogger("SOME")
                    override fun execute() {

        (TF_LogicalOrOp
          (TF_GreaterOp
            (TF_SubOp:$fraction $input, (TF_FloorOp:$round_val $input)),
            (TF_ConstOp:$half (GetScalarOfFloatType<"0.5"> $input))
          ),
          (TF_LogicalAndOp
            // If the input is equal to 0.5 ...
            (TF_EqualOp
              $fraction,
              $half,
              /*incompatible_shape_error*/ConstBoolAttrTrue

//     sign_bit exponent_bits fraction_bits
//
//   Here, sign_bit is a single bit that designates the sign of the
//   number.
//
//   For float, there are 8 exponent bits and 23 fraction bits.
//
//   For double, there are 11 exponent bits and 52 fraction bits.
//
//   More details can be found at
//   http://en.wikipedia.org/wiki/IEEE_floating-point_standard.
//
// Template parameter:
//

   * the data points and the best fit line). For this fit, this error is a fraction {@code sqrt(1 -
   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a

   * the data points and the best fit line). For this fit, this error is a fraction {@code sqrt(1 -
   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a