return nullptr;
  }
  tensorflow::PartialTensorShape partial_shape;
  if (shape.num_dims != -1) {
    DCHECK(shape.dim_sizes != nullptr);
    Status status = tensorflow::PartialTensorShape::MakePartialShape(
        reinterpret_cast<int64_t*>(shape.dim_sizes), shape.num_dims,
        &partial_shape);
    if (!status.ok()) {
      tsl::Set_TF_Status_from_Status(s, status);

  TF_OperationGetAttrShape(oper, "v", nullptr, 10, s_);
  EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  // Partially specified shape
  const int64_t partial_shape[] = {17, -1};
  const size_t sz = TF_ARRAYSIZE(partial_shape);
  desc = init("shape");
  TF_SetAttrShape(desc, "v", partial_shape, sz);
  oper = TF_FinishOperation(desc, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

#include "tensorflow/core/framework/log_memory.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"  // NOLINT
#include "tensorflow/core/framework/tensor_shape.h"

*   `tf.distribute.experimental.MultiWorkerMirroredStrategy` adds support for
    partial batches. Workers running out of data now continue to participate in
    the training with empty inputs, instead of raising an error. Learn more
    about
    [partial batches here](https://www.tensorflow.org/tutorials/distribute/input#partial_batches).
*   Improve the performance of reading metrics eagerly under