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>
 *

  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"]} {

  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,

    def setup() {
        INVOCATION_COUNT.set(0)
    }

    def "input artifact selection is restored when using the in-memory cache"() {
        def transform = registerTransform(IdentityTransform)

        def inputArtifact1 = file("input1/input.txt")
        inputArtifact1.text = "Hello"

        def inputArtifact2 = file("input2/input.txt")
        inputArtifact2.text = "Hello"

        expect:

  DummyDevice dummy_device(nullptr);
  params.device = &dummy_device;
  params.op_kernel = kernel.get();
  gtl::InlinedVector<TensorValue, 4> inputs;
  inputs.emplace_back(input_tensor);
  params.inputs = inputs;

  OpKernelContext ctx(&params);
  kernel->Compute(&ctx);
  ASSERT_EQ(expected_code, ctx.status().code());
  if (expected_code == error::OK) {
    ASSERT_EQ(expected_shape, ctx.mutable_output(0)->shape())

        
        input2 = getResourceFile("2.txt")
        output2 = signing.signatureType.fileFor(input2)
        input2Artifact = new DefaultPublishArtifact(input2.name, "Text File", "txt", null, null, input2)
        
        [output1, output2].each { output ->
            assert !output.exists() || output.delete()
        }
        
        assert input1.text && input2.text  // don't care what it is, just need some
    }
    

// Copyright 2013 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// initialization cycles

package init0

// initialization cycles (we don't know the types)
const (
	s0 /* ERROR "initialization cycle: s0 refers to itself" */ = s0

	x0 /* ERROR "initialization cycle for x0" */ = y0
	y0 = x0

	a0 = b0

    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"