TFE_TensorHandleResolve(handle, status.get()), TF_DeleteTensor);
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  ASSERT_EQ(TF_TensorType(actual_value.get()),
            static_cast<TF_DataType>(DataTypeToEnum<value_type>().value));
  EXPECT_EQ(expected_value,
            *static_cast<value_type*>(TF_TensorData(actual_value.get())));
}

// unknown dimension is represented by `-1`.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.
//   * `num_dims` does not match the actual number of dimensions.
TF_CAPI_EXPORT extern void TF_GraphGetTensorShape(TF_Graph* graph,
                                                  TF_Output output,

// if the status is not ok.
TF_CAPI_EXPORT extern int TF_OpIsStateful(const char* op_type,
                                          TF_Status* status);

// Platform specific initialization routine. Very few platforms actually require
// this to be called.
TF_CAPI_EXPORT void TF_InitMain(const char* usage, int* argc, char*** argv);

// Platform-specific implementation to return an unused port. (This should used
// in tests only.)

// A create function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void* create(TF_OpKernelConstruction* ctx) {
  TF_DataType type;
  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's

//
// If async execution is enabled, the call may simply enqueue the execution
// and return "non-ready" handles in `retvals`. Note that any handles contained
// in 'op' should not be mutated till the kernel execution actually finishes.
//
// For sync execution, if any of the inputs to `op` are not ready, this call
// will block till they become ready and then return when the kernel execution
// is done.

// Represents either a MlirTensor or a GraphTensor.
// This base class does not expose any public methods other than to distinguish
// which subclass it actually is. The user is responsible to use the right
// type of AbstractTensor in their context (do not pass an MlirTensor to a
// GraphContext and vice-versa).
class TracingTensorHandle : public AbstractTensorHandle {
 protected:

// through TF_OpKernelContext, and is opaque to plugin.
typedef struct TF_Device TF_Device;

typedef struct TF_VariableInfo TF_VariableInfo;

// Returns a `TF_Device` pointer, which actually points to a C++ `Device`.
// Currently we only allow `NextPluggableDevice` to be casted as `TF_Device`,
// but in theory every this is a C API for every kind of device.

typedef struct TF_Filesystem_Option {
  char* name;                         // null terminated, owned
  char* description;                  // null terminated, owned
  int per_file;                       // bool actually, but bool is not a C type
  TF_Filesystem_Option_Value* value;  // owned
} TF_Filesystem_Option;

/// SECTION 2. Function tables for functionality provided by plugins