# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Top-level module of TensorFlow. By convention, we refer to this module as
`tf` instead of `tensorflow`, following the common practice of importing
TensorFlow via the command `import tensorflow as tf`.

// In the TFv1 convention, TF_Tensor can hold a string serialization of
// DT_RESOURCE. The string serialization is converted back to a
// ResourceHandle during Session run where the TF_Tensor is converted to a
// Tensor.
// TFv2 does not depend on this conversion. There is no matching
// TF_TensorFromTensorV1 because the conversion to string is performed by the
// python side of Session.

```

### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:

- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.

### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.

         * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
         * All you need is a good internet connection and you are all set.
      * Try to build TF from sources by changing CPU optimization flags.
   
   *Please let us know if this helps.*
   
windows_comment: >

TF_CAPI_EXPORT extern void TFE_ContextEndStep(TFE_Context* ctx);

#ifdef __cplusplus
} /* end extern "C" */
#endif

#ifdef __cplusplus
// A workaround to ease conversion to and from numpy objects and
// TFE_TensorHandle's.
//
// TODO(ashankar): Figure out an alternative scheme that precludes the need for
// these API-boundary breaking methods.
namespace tensorflow {
class Tensor;

        "//tensorflow/core:protos_all_cc",
        # TODO(b/74620627): remove when _USE_C_SHAPES is removed
    ],
    alwayslink = 1,
)

cc_library(
    name = "conversion_macros",
    hdrs = [
        "conversion_macros.h",
    ],
    visibility = ["//tensorflow:__subpackages__"],
)

cc_library(
    name = "c_op_requires",
    hdrs = ["c_op_requires.h"],

// --------------------------------------------------------------------------
// C API for Graph. The API is under active development and eventually
// should allow registering a plugin graph optimizer with TensorFlow.
//
// Conventions:
//   * Struct prefix indicates whether struct fields should be filled by the
//     plugin or core implementation:
//     * Struct that should be filled by the plugin: `TP_OptimizerConfigs`,

    int64_t init_timeout_in_ms, TF_Status* status,
    bool clear_existing_contexts);

// Set server def with retries and timeout. This is helpful for fault-tolerant
// initial connection in high-preemption environments, such as
// ParameterServerStrategy training.
// This API is for experimental usage and may be subject to change.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeoutAndRetries(

    * when converter flag "_experimenal_use_buffer_offset" is enabled, additional metadata is automatically excluded from the generated model. The behaviour is the same as "exclude_conversion_metadata" is set
    * If the model is larger than 2GB, then we also require "exclude_conversion_metadata" flag to be set

### Major Features and Improvements

    deps = [
        ":immediate_execution_context",
        "//tensorflow/c:conversion_macros",
    ],
)

cc_library(
    name = "tfe_cancellation_manager_internal",
    hdrs = ["tfe_cancellation_manager_internal.h"],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        "//tensorflow/c:conversion_macros",
        "//tensorflow/core:framework",
    ],
)

cc_library(