GraphDef

 EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
 "library" provides user-defined functions.
 Naming:
   * library.function.name are in a flat namespace.
     NOTE: We may need to change it to be hierarchical to support
     different orgs. E.g.,
     { "/google/nn", { ... } },
     { "/google/vision", { ... } }
     { "/org_foo/module_bar", { ... } }
     map<string, FunctionDefLib> named_lib;
   * If node[i].op is the name of one function in "library",
     node[i] is deemed as a function call. Otherwise, node[i].op
     must be a primitive operation supported by the runtime.
 Function call semantics:
   * The callee may start execution as soon as some of its inputs
     are ready. The caller may want to use Tuple() mechanism to
     ensure all inputs are ready in the same time.
   * The consumer of return values may start executing as soon as
     the return values the consumer depends on are ready.  The
     consumer may want to use Tuple() mechanism to ensure the
     consumer does not start until all return values of the callee
     function are ready.
 

 EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
 "library" provides user-defined functions.
 Naming:
   * library.function.name are in a flat namespace.
     NOTE: We may need to change it to be hierarchical to support
     different orgs. E.g.,
     { "/google/nn", { ... } },
     { "/google/vision", { ... } }
     { "/org_foo/module_bar", { ... } }
     map<string, FunctionDefLib> named_lib;
   * If node[i].op is the name of one function in "library",
     node[i] is deemed as a function call. Otherwise, node[i].op
     must be a primitive operation supported by the runtime.
 Function call semantics:
   * The callee may start execution as soon as some of its inputs
     are ready. The caller may want to use Tuple() mechanism to
     ensure all inputs are ready in the same time.
   * The consumer of return values may start executing as soon as
     the return values the consumer depends on are ready.  The
     consumer may want to use Tuple() mechanism to ensure the
     consumer does not start until all return values of the callee
     function are ready.
 

repeated .tensorflow.NodeDef node = 1;

repeated .tensorflow.NodeDef node = 1;

repeated .tensorflow.NodeDef node = 1;

repeated .tensorflow.NodeDef node = 1;

repeated .tensorflow.NodeDef node = 1;

 Deprecated single version field; use versions above instead.  Since all
 GraphDef changes before "versions" was introduced were forward
 compatible, this field is entirely ignored.
 

 Compatibility versions of the graph.  See core/public/version.h for version
 history.  The GraphDef version is distinct from the TensorFlow version, and
 each release of TensorFlow will support a range of GraphDef versions.
 

 Compatibility versions of the graph.  See core/public/version.h for version
 history.  The GraphDef version is distinct from the TensorFlow version, and
 each release of TensorFlow will support a range of GraphDef versions.
 

 EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
 "library" provides user-defined functions.
 Naming:
   * library.function.name are in a flat namespace.
     NOTE: We may need to change it to be hierarchical to support
     different orgs. E.g.,
     { "/google/nn", { ... } },
     { "/google/vision", { ... } }
     { "/org_foo/module_bar", { ... } }
     map<string, FunctionDefLib> named_lib;
   * If node[i].op is the name of one function in "library",
     node[i] is deemed as a function call. Otherwise, node[i].op
     must be a primitive operation supported by the runtime.
 Function call semantics:
   * The callee may start execution as soon as some of its inputs
     are ready. The caller may want to use Tuple() mechanism to
     ensure all inputs are ready in the same time.
   * The consumer of return values may start executing as soon as
     the return values the consumer depends on are ready.  The
     consumer may want to use Tuple() mechanism to ensure the
     consumer does not start until all return values of the callee
     function are ready.
 

 Compatibility versions of the graph.  See core/public/version.h for version
 history.  The GraphDef version is distinct from the TensorFlow version, and
 each release of TensorFlow will support a range of GraphDef versions.