"Filesystem for ", dir, " does not support GetChildren()"));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  std::string translated_name = TranslateName(dir);
  // Note that `children` is allocated by the plugin and freed by core
  // TensorFlow, so we need to use `plugin_memory_free_` here.
  char** children = nullptr;
  const int num_children =

  }

  // Check for inconsistent state. If there is a block later in the same file
  // in the cache, and our current block is not block size, this likely means
  // we have inconsistent state within the cache. Note: it's possible some
  // incomplete reads may still go undetected.
  if (block->data.size() < block_size_) {
    Key fmax = std::make_pair(key.first, std::numeric_limits<size_t>::max());

        static_cast<const float*>(TF_TensorData(resolved));
    EXPECT_EQ(0, memcmp(m_float, resolved_data, 4 * sizeof(float)));
    TF_DeleteTensor(resolved);
    TFE_DeleteTensorHandle(copy_aliased);  // Note that this will delete copy.
    TFE_DeleteTensorHandle(on_host);
  }
  TF_DeleteDeviceList(devices);
  TF_DeleteTensor(m_data);
  TFE_DeleteTensorHandle(m);
  TFE_DeleteContext(ctx);
  TF_DeleteStatus(status);

  TF_DeleteAbstractOp(add_op);

  TF_AbstractFunction* func =
      TF_FinalizeFunction(graph_ctx, add_outputs, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  // Note: TF_OutputList does not own the underlying AbstractTensors, those
  // need to be deleted explicitly.
  TF_DeleteAbstractTensor(TF_OutputListGet(add_outputs, 0));

  // Build eager context.

    TF_Status* status);

// Controls how to act when we try to run an operation on a given device but
// some input tensors are not on that device.
// LINT.IfChange
// Note: Keep in sync with internal copy of enum in eager/context.h.
typedef enum TFE_ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  TFE_DEVICE_PLACEMENT_EXPLICIT = 0,

-   If a change is needed, the contributor is requested to make the suggested
    change.
-   You make the change and submit it for the review again.
-   This cycle repeats itself until the PR gets approved.
-   Note: As a friendly reminder, we may reach out to you if the PR is awaiting
    your response for more than 2 weeks.

**4. Approved**

-   Once the PR is approved, it gets `kokoro:force-run` label applied and it

// policy is applied to any subsequent op executions.
TF_CAPI_EXPORT void TFE_ContextSetSoftDevicePlacement(TFE_Context* ctx,
                                                      unsigned char enable,
                                                      TF_Status* status);

// Configure device placement policy logging for the eager executor. Note this

  bool valid = true;
  int64_t expected_stride = 1;
  for (int i = ndim - 1; i >= 0; --i) {
    // Empty tensors are always compact regardless of strides.
    if (shape_arr[i] == 0) return true;
    // Note that dimensions with size=1 can have any stride.
    if (shape_arr[i] != 1 && stride_arr[i] != expected_stride) {
      valid = false;
    }
    expected_stride *= shape_arr[i];
  }
  return valid;
}

//       `struct_size` as well, along with all other fields.
//     * Refer to "TensorFlow Versioning Strategy" section at
//       https://github.com/tensorflow/community/pull/257/files.
//     * Note that the API is still under active development and doesn't have
//       versioning guarantees yet.
//   * `void* ext` is a free-form field that can be populated by

#include "tensorflow/core/util/device_name_utils.h"

namespace tensorflow {
class EagerExecutor;
class EagerContext;
class CustomDevice;
class CustomDeviceOpHandler;
class Device;

// LINT.IfChange
// Note: Keep in sync with exported copy of enum in eager/c_api.h.
enum ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  DEVICE_PLACEMENT_EXPLICIT = 0,