edium}.uk-animation-slide-left-medium{animation-name:uk-fade-left-medium}.uk-animation-slide-right-medium{animation-name:uk-fade-right-medium}.uk-animation-kenburns{animation-name:uk-scale-kenburns;animation-duration:15s}.uk-animation-shake{animation-name:uk-shake}.uk-animation-stroke{animation-name:uk-stroke;stroke-dasharray:var(--uk-animation-stroke);animation-duration:2s}.uk-animation-reverse{animation-direction:reverse;animation-timing-function:ease-in}.uk-animation-fast{animation-duration:....

// 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 {

# 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

		}
	} else {
		// Note: can't use rawmem (which avoids zeroing of memory), because then GC can scan uninitialized memory.
		to = mallocgc(tomem, et, true)
		if copymem > 0 && writeBarrier.enabled {
			// Only shade the pointers in old.array since we know the destination slice to
			// only contains nil pointers because it has been cleared during alloc.
			//
			// It's safe to pass a type to this function as an optimization because

		runnext := atomic.Loaduintptr((*uintptr)(unsafe.Pointer(&pp.runnext)))
		if tail == atomic.Load(&pp.runqtail) {
			return head == tail && runnext == 0
		}
	}
}

// To shake out latent assumptions about scheduling order,
// we introduce some randomness into scheduling decisions
// when running with the race detector.
// The need for this was made obvious by changing the

  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);

  if (pack_axis < 0) {
    pack_axis += rank;
  }

  // Concat out shape.
  for (int i = 0; i < rank; ++i) {
    int64_t dim_size = input_type.getDimSize(i);
    if (i == pack_axis) {
      dim_size *= count;
    }
    concat_out_shape.push_back(dim_size);
  }

  // Pack out shape.
  int j = 0;
  for (int i = 0; i < rank + 1; ++i) {
    if (i == pack_axis) {

  }
  func.return
}

// CHECK: %[[SHAPE:[0-9]*]] = "tf.Shape"(%[[ARG_0]])
// CHECK: %[[OP_A:[0-9]*]] = "tf.opA"(%[[SHAPE]])
// CHECK: %[[OP_B:[0-9]*]] = "tf.opB"(%[[SHAPE]], %[[OP_A]])
// CHECK: tf_device.replicate
// CHECK:   tf_device.return %[[SHAPE]], %[[OP_A]], %[[OP_B]]


// CHECK-LABEL: func @nested_ops