// CHECK-NEXT:    } ],
// CHECK-NEXT:    inputs: [ 0, 1 ],
// CHECK-NEXT:    outputs: [ 6, 5 ],
// CHECK-NEXT:    operators: [ {
// CHECK-NEXT:      inputs: [ 0, 3, 2 ],
// CHECK-NEXT:      outputs: [ 5 ],
// CHECK-NEXT:      builtin_options_type: FullyConnectedOptions,
// CHECK-NEXT:      builtin_options: {
// CHECK-EMPTY:
// CHECK-NEXT:      }
// CHECK-NEXT:    }, {
// CHECK-NEXT:      inputs: [ 0, 4, 2 ],

    }

    def "out-of-date when input file changes"() {
        given:
        def inputFile = file("input.txt")
        inputFile.text = "foo"
        buildFile << """
            ${keyInfo.addAsPropertiesScript()}
            signing {
                ${signingConfiguration()}
            }
            task signCustomFile(type: Sign) {
                sign(file("input.txt"))
            }
        """

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
}

# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=4 -tf-input-data-types=DT_INT32 -tf-output-arrays=output %s -o - --output-mlir | FileCheck %s

node {
  name: "default"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 0
      }
    }
  }
}

# Verify that we match correctly the input / output when they are scalar.

# CHECK-LABEL: func @main
# CHECK-SAME:  (%{{[a-z0-9]+}}: tensor<f32> {tf.device = "/device:CPU:0"})
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "out:1,out"

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
}

  func.return %2 : tensor<3x2xf32>
}

// -----

// CHECK-LABEL: func @uniform_quantized_add_bias_not_const
func.func @uniform_quantized_add_bias_not_const(%input1: tensor<3x2xi32>, %input2: tensor<2xi32>) -> tensor<3x2xi32> {
  %input_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
  %input_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>

 * 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>
 *

        where:
        inputAnnotation   | inputType             | inputPath         | inputToPrint
        "@InputDirectory" | "DirectoryProperty"   | "input"           | "getInput().file('input.txt').get().asFile.text"
        "@InputFile"      | "RegularFileProperty" | "input/input.txt" | "getInput().get().asFile.text"
    }

  func.func @add(%arg0: tensor<?xf32> {tf_saved_model.index_path = ["input1"]}, %arg1: tensor<?xf32> {tf_saved_model.index_path = ["input2"]}) -> (tensor<?xf32> {tf_saved_model.index_path = ["result"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "input1:0,input2:0", outputs = "result:0"}, tf_saved_model.exported_names = ["add"]} {