}

  template <typename T>
  void Run(const std::vector<Input::Initializer>& input_values,
           const std::vector<T>& expected_output_values) {
    ClientSession session(scope_);

    DCHECK_EQ(input_values.size(), inputs_.size());
    ClientSession::FeedType feeds;
    for (int i = 0; i < inputs_.size(); ++i) {
      feeds.emplace(inputs_[i], input_values[i]);
    }

    std::vector<Tensor> out_tensors;

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-tpu-annotate-dynamic-shape-inputs %s | FileCheck %s

// Test that annotate the inputs of the cluster func to be dynamic shaped.

module attributes {tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU_SYSTEM:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
   func.func @main(
      %arg0: tensor<2048xi64> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"},

  shape_0, shape_1 = tf.shape_n([op.inputs[0], op.inputs[1]])
  return [
      tf.compat.v1.nn.conv2d_backprop_input(
          shape_0,
          op.inputs[1],
          grad,
          strides=strides,
          padding=padding,
          dilations=dilations,
          data_format='NHWC'),
      tf.compat.v1.nn.conv2d_backprop_filter(
          op.inputs[0],
          shape_1,
          grad,

package norm

import "unicode/utf8"

type input struct {
	str   string
	bytes []byte
}

func inputBytes(str []byte) input {
	return input{bytes: str}
}

func inputString(str string) input {
	return input{str: str}
}

func (in *input) setBytes(str []byte) {
	in.str = ""
	in.bytes = str
}

func (in *input) setString(str string) {
	in.str = str
	in.bytes = nil
}

 * limitations under the License.
 */
package org.gradle.api.tasks;

import java.lang.annotation.*;

/**
 * <p>Attached to a task property to indicate that the property specifies some input value for the task.</p>
 *
 * <p>This annotation should be attached to the getter method in Java or the property in Groovy.
 * Annotations on setters or just the field in Java are ignored.</p>
 *

@__forward_call_369(%arg0: tensor<16x224x224x3xf32> {tf._user_specified_name = "inputs"}, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<*x!tf_type.resource>, %arg3: tensor<*x!tf_type.resource>, %arg4: tensor<*x!tf_type.resource>) -> (tensor<?x?xf32>, tensor<*xf32>, tensor<?x16384xf32>, tensor<16x112x112x?xf32>, tensor<16x224x224x3xf32>, tensor<*xf32>) attributes {tf.entry_function = {control_outputs = "", inputs = "inputs_0,conv1_conv2d_readvariableop_resource,conv1_biasadd_readvariableop_resource...

        inputs.property('a', 'value')

        then:
        inputs.hasInputs
        !inputs.hasSourceFiles
    }

    def hasInputsWhenEmptySourceFilesRegistered() {
        when:
        inputs.files([]).skipWhenEmpty()

        then:
        inputs.hasInputs
        inputs.hasSourceFiles
    }

    def hasInputsWhenSourceFilesRegistered() {
        when:
        inputs.file('a').skipWhenEmpty()

    def "incremental task with NAME_ONLY inputs (matching file names and content) detects deleted file"() {
        def inputs = folderNames().collect { file("${it}/input.txt").createFile() }

        buildFile << incrementalTaskWithNameOnlyInputFiles(inputs)
        run INCREMENTAL_TASK_NAME

        when:
        inputs[1..2]*.delete()
        println "${inputs} exists: ${inputs*.exists()}"
        run INCREMENTAL_TASK_NAME, "--info"

    private final String[] inputs;

    private final String output;

    private String[] newInputs;

    private String newOutput;

    public CharMappingItem(final long id, final String[] inputs, final String output) {
        this.id = id;
        this.inputs = inputs;
        this.output = output == null ? null : output.replace("\n", " ");
        Arrays.sort(inputs);

        if (id == 0) {

    protected AbstractModelAction(ModelReference<T> subject, ModelRuleDescriptor descriptor, ModelReference<?>... inputs) {
        this(subject, descriptor, inputs == null ? EMPTY_MODEL_REF_LIST : Arrays.asList(inputs));
    }

    protected AbstractModelAction(ModelReference<T> subject, ModelRuleDescriptor descriptor, List<? extends ModelReference<?>> inputs) {