module {
func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> attributes {tf.entry_function = {inputs = "input0,input1,input2,input3", outputs = "output"}} {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input -tf-input-shapes='' -tf-output-arrays=input -tf-convert-legacy-fed-inputs -o - | FileCheck --check-prefix=NODATATYPE %s

# Verify that invalid LegacyFedInput ops without any inputs are replaced with
# Placeholder ops.

  TF_Session* mutable_session() { return session_; }

 private:
  void DeleteInputValues();
  void ResetOutputValues();

  TF_Session* session_;
  std::vector<TF_Output> inputs_;
  std::vector<TF_Tensor*> input_values_;
  std::vector<TF_Output> outputs_;
  std::vector<TF_Tensor*> output_values_;
  std::vector<TF_Operation*> targets_;
};

// 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"},

    }

    self._assertOpAndComposite([input_, filter_, bias],
                               tf.function(gen_mnist_ops.new_conv2d),
                               ops_defs._composite_conv_add_relu, kwargs)

  def test_new_conv2d_relu6(self):
    input_ = tf.random.uniform([1, 4, 4, 1])
    filter_ = tf.random.uniform([2, 2, 1, 8])
    bias = tf.zeros([8])
    kwargs = {
        'input_': input_,
        'filter_': filter_,

        private final ModelType<T> type;
        private final List<ModelReference<?>> inputs;

        private UnmanagedElementCreationAction(ModelRuleDescriptor descriptor, ModelReference<?> subject, List<ModelReference<?>> inputs, ModelType<T> type) {
            this.subject = subject;
            this.inputs = inputs;
            this.descriptor = descriptor;
            this.type = type;
        }

        @Override

            this.inputs = inputs;
            this.ruleSourceType = ruleSourceType;
            this.ruleExtractor = ruleExtractor;
        }

        @Override
        public ModelReference<?> getSubject() {
            return targetReference;
        }

        @Override
        public List<? extends ModelReference<?>> getInputs() {
            return inputs;
        }

        @Override

          absl::StrCat("tensor parsing error: ", alias));
    }

    inputs.emplace_back(feed_name, tensor);
  }

  if (!request_inputs.empty() &&
      seen_request_inputs.size() != request_inputs.size()) {
    return absl::InvalidArgumentError(absl::StrCat(
        "Inputs contains invalid name. Used request inputs: ",
        absl::StrJoin(seen_request_inputs, ","),

            inputs.add(input);
        }
        return inputs.build();
    }

    private static DataSource packSample(String name, List<DataSource> inputs, Packer packer, DataAccessor accessor) throws IOException {
        long sumLength = 0;
        for (DataSource input : inputs) {
            sumLength += input.getLength();
        }

=== Build cycles
Gradle starts watching for changes just before a task executes.
If a task modifies its own inputs while executing, Gradle will detect the change and trigger a new build.
If every time the task executes, the inputs are modified again, the build will be triggered again.
This isn't unique to continuous build.
A task that modifies its own inputs will never be considered up-to-date when run "normally" without continuous build.