TF_Tensor* a =
      TF_AllocateTensor(TF_UINT64, dims, 2, 6 * TF_DataTypeSize(TF_UINT64));
  TF_Tensor* b =
      TF_AllocateTensor(TF_UINT64, nullptr, 0, TF_DataTypeSize(TF_UINT64));
  EXPECT_NE(a, nullptr);
  EXPECT_NE(b, nullptr);

  EXPECT_EQ(6, TF_TensorElementCount(a));
  EXPECT_EQ(1, TF_TensorElementCount(b));
  EXPECT_EQ(6 * TF_DataTypeSize(TF_UINT64), TF_TensorByteSize(a));

    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    X.reset(X_raw);
  }
  // Label
  int32_t Y_vals[] = {1, 0, 1};
  int64_t Y_dims[] = {3};
  AbstractTensorHandlePtr Y;
  {
    AbstractTensorHandle* Y_raw;
    status_ = TestTensorHandleWithDims<int32_t, TF_INT32>(
        immediate_execution_ctx_.get(), Y_vals, Y_dims, 1, &Y_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();

==============================================================================*/

#include <limits.h>
#include <memory.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <unistd.h>

#ifdef _WIN32
#include <process.h>
#endif

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_experimental.h"
#include "tensorflow/c/env.h"
#include "tensorflow/c/kernels.h"

*   `tf.lite`:

    *   Added TFLite builtin op support for the following TF ops:
        *   `tf.raw_ops.Bucketize` op on CPU.
        *   `tf.where` op for data types
            `tf.int32`/`tf.uint32`/`tf.int8`/`tf.uint8`/`tf.int64`.
        *   `tf.random.normal` op for output data type `tf.float32` on CPU.
        *   `tf.random.uniform` op for output data type `tf.float32` on CPU.

               tensorflow::uint64 send_device_incarnation) {
  TF_Status* status = TF_NewStatus();
  TFE_Op* op = TFE_NewOp(ctx, op_name.c_str(), status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);

  TFE_OpSetAttrType(op, "tensor_type", TF_INT32);
  TFE_OpSetAttrString(op, "tensor_name", "dummy", 5);

TF_DEVICELIST_METHOD(const char*, TF_DeviceListType, device_type().c_str(),
                     nullptr);
TF_DEVICELIST_METHOD(int64_t, TF_DeviceListMemoryBytes, memory_limit(), -1);
TF_DEVICELIST_METHOD(uint64_t, TF_DeviceListIncarnation, incarnation(), 0);

#undef TF_DEVICELIST_METHOD

}  // end extern "C"

// --------------------------------------------------------------------------
// New Graph and Session API

// is initialized (either locally or remotely). The context_id can change during
// the process lifetime although this should cause the worker to be
// reinitialized (e.g. cleared caches) as well.
TF_CAPI_EXPORT extern uint64_t TFE_GetContextId(TFE_Context* ctx);

// -----------------------------------------------------------------------------
// Cancellation APIs.

typedef struct TFE_CancellationManager TFE_CancellationManager;

  tensorflow::unwrap(ctx)->SetShouldStoreGraphs(true);
}

void TFE_ContextDisableGraphCollection(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->SetShouldStoreGraphs(false);
}

uint64_t TFE_GetContextId(TFE_Context* ctx) {
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  return context->GetContextId();
}

    // more than fits in a 32-bit integer.
    size_t requested_read_length;
    if (n > INT32_MAX)
      requested_read_length = INT32_MAX;
    else
      requested_read_length = n;

    // `pread` returns a `ssize_t` on POSIX, but due to interface being
    // cross-platform, return type of `Read` is `int64_t`.
    int64_t r = int64_t{pread(posix_file->fd, dst, requested_read_length,

    case DT_FLOAT:
    case DT_DOUBLE:
    case DT_COMPLEX64:
    case DT_COMPLEX128:
    case DT_RESOURCE:
    case DT_VARIANT:
      return true;
    case DT_QINT8:
    case DT_QINT16:
    case DT_QINT32:
    case DT_QUINT8:
    case DT_QUINT16:
      return tensorflow::flags::Global()
          .enable_quantized_dtypes_training.value();
    default:
      return false;
  }
}