# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME:   {{.*}})
# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key2"]


def Test():

  x = tf.constant(1.0, shape=(3, 3))
  y = tf.constant(1.0, shape=(3, 3))

  s = tf.transpose(x)
  t = tf.transpose(y)

  tensor_info_s = tf.compat.v1.saved_model.utils.build_tensor_info(s)
  tensor_info_t = tf.compat.v1.saved_model.utils.build_tensor_info(t)

Tres Popp <******@****.***> 1648061556 -0700

// Copy the internal data representation of `from` to `to`. `new_dims` and
// `num_new_dims` specify the new shape of the `to` tensor, `type` specifies its
// data type. On success, *status is set to TF_OK and the two tensors share the
// same data buffer.
//
// This call requires that the `from` tensor and the given type and shape (dims
// and num_dims) are "compatible" (i.e. they occupy the same number of bytes).

// Copyright 2024 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.

package p

// This type and the following one will share the same GC shape and size.
type Pointery struct {
	p *Pointery
	x [1024]int
}

type Pointery2 struct {
	p *Pointery2
	x [1024]int
}

// This type and the following one will have the same size.
type Vanilla struct {

  // name that has not been used so far in a scope will get no suffix. Later
  // uses of the same name will get suffixes _1, _2, _3, etc. Multiple scopes
  // can share the same NameMap. For instance, a new scope created using
  // WithControlDependencies() would share the same NameMap with the parent.
  typedef std::unordered_map<string, int> NameMap;

  Impl(const std::shared_ptr<Graph>& graph,
       const std::shared_ptr<Status>& status,

# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key2"]


def Test():

  x = tf.constant([[1.0], [1.0], [1.0]])
  y = tf.get_variable(
      name='y',
      shape=(1, 3),
      initializer=tf.random_normal_initializer(),
      trainable=True)
  r = tf.matmul(x, y)

  tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
  tensor_info_r = tf.saved_model.utils.build_tensor_info(r)

namespace tensorflow {

class XlaDevice : public LocalDevice {
 public:
  // Given a tensor, sets `xla::Shape*` the shape of tensor's representation
  // on device, fully padded. On error, the contents of `xla::Shape*`
  // are undefined.
  typedef std::function<Status(const Tensor&, xla::Shape*)> PaddedShapeFn;

  // Wrapper class to store metadata about the XlaDevice, where it can be

 */

package org.gradle.api.internal.artifacts.ivyservice.resolveengine.excludes.factories

import groovy.json.JsonSlurper

class ExcludeJsonLogToCode {
    private final static List<String> WORDS = [
        "shake",
        "grain",
        "root",
        "rain",
        "cover",
        "crib",
        "lunchroom",
        "sort",
        "building",
        "fact",
        "grass",
        "planes",

TEST_F(NNGradTest, SoftmaxGrad) {
  TensorShape shape({32, 10});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Softmax(scope_, x);
  RunTest(x, shape, y, shape);
}

TEST_F(NNGradTest, SoftmaxRank3Grad) {
  TensorShape shape({32, 1, 10});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto y = Softmax(scope_, x);
  RunTest(x, shape, y, shape);
}

  TensorShape shape({2, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto indices = Const(scope_, {{1}, {0}});
  auto y = GatherNd(scope_, x, indices);
  RunTest(x, shape, y, shape);
}

TEST_F(ArrayGradTest, GatherNdGrad_SliceIndexing_Int64) {
  TensorShape shape({2, 2});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto indices = Cast(scope_, Const(scope_, {{1}, {0}}), DT_INT64);