if (!(source instanceof FileTree)) {
            throw new UnsupportedOperationException(String.format("Can only add FileTree instances to %s.", getDisplayName()));
        }

        sourceTrees.add(Cast.cast(FileTreeInternal.class, source));
    }

  func.func @main(
      %arg0: tensor<10xf32>, %arg1: tensor<10xf32>, %arg2: tensor<6xi32>
    ) -> (tensor<10xf32>, tensor<6xi32>) {
    // CHECK: %[[CAST_0:.*]] = "tf.Cast"(%[[ARG_0]]) <{Truncate = false}> : (tensor<10xf32>) -> tensor<10xbf16>
    // CHECK: %[[CAST_1:.*]] = "tf.Cast"(%[[ARG_1]]) <{Truncate = false}> : (tensor<10xf32>) -> tensor<10xbf16>
    // CHECK: %[[RESULT:.*]]:2 = "tf.XlaCallModule"(%[[CAST_0]], %[[CAST_1]], %[[ARG_2]])

    %cst_0 = "tf.Const"() {value = dense<0.0157480314> : tensor<f32>} : () -> tensor<f32>
    %0 = "tf.Identity"(%cst) : (tensor<2x1024xi8>) -> tensor<2x1024xi8>
    %1 = "tf.Cast"(%0) {Truncate = false} : (tensor<2x1024xi8>) -> tensor<2x1024xf32>

      /*effective_hidden_scale_intermediate=*/mlir::TypeAttr());

  // Cast the static shaped lstm result to FuncOp's signature -
  // Ranked but unknown 2nd dimension to support stacking these.
  SmallVector<int64_t, 2> func_output_shape = {1, tensorflow::kTFDynamicSize};
  auto func_result_type = tensorflow::GetTypeFromTFTensorShape(
      func_output_shape,
      mlir::cast<RankedTensorType>(input_.getType()).getElementType());

LogicalResult VerifyTensorTypesCompatible(Type t1, Type t2) {
  if (!mlir::isa<TensorType>(t1) || !mlir::isa<TensorType>(t2)) {
    return failure();
  }
  return verifyCompatibleShape(mlir::cast<TensorType>(t1),
                               mlir::cast<TensorType>(t2));
}

LogicalResult GlobalTensorOp::verify() {
  GlobalTensorOp global_tensor = *this;
  if (global_tensor.getValue()) {
    if (failed(VerifyTensorTypesCompatible(

  CPred<"quant::ReshapableTo1DTensor($0.getType().cast<ShapedType>())">,
  "Checks if the value dims are all ones except the right most dim">;

def ReshapeTo1DTensor : NativeCodeCall<
  "quant::ReshapeTo1DTensor($_builder, $_loc, $0)">;

def HasEqualShape : Constraint<CPred<
  "$0.getType().cast<ShapedType>().hasRank() && "
  "$1.getType().cast<ShapedType>().hasRank() && "

            // Kotlin passes these types for its own basic types
            return Cast.uncheckedNonnullCast(mapProperty(JavaReflectionUtil.getWrapperTypeForPrimitiveType(keyType), valueType));
        }
        if (valueType.isPrimitive()) {
            // Kotlin passes these types for its own basic types
            return Cast.uncheckedNonnullCast(mapProperty(keyType, JavaReflectionUtil.getWrapperTypeForPrimitiveType(valueType)));

            return defaultArgsSerializer.forTypes(types);
        }

        final Serializer<Object>[] serializers = Cast.uncheckedNonnullCast(new Serializer<?>[types.length]);
        for (int i = 0; i < types.length; i++) {
            Class<?> type = types[i];
            serializers[i] = Cast.uncheckedNonnullCast(selected.build(type));
        }
        return new ArraySerializer(serializers);
    }

import org.gradle.api.internal.tasks.TaskExecutionContext;
import org.gradle.api.internal.tasks.TaskExecutionOutcome;
import org.gradle.api.internal.tasks.TaskStateInternal;
import org.gradle.api.specs.Spec;
import org.gradle.internal.Cast;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * A {@link org.gradle.api.internal.tasks.TaskExecuter} which skips tasks whose onlyIf predicate evaluates to false
 */

import com.google.common.collect.SetMultimap;
import org.gradle.api.internal.project.HoldsProjectState;
import org.gradle.api.internal.project.ProjectInternal;
import org.gradle.internal.Cast;
import org.gradle.util.Path;

import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.List;