CopyToCore<TF_ReadOnlyMemoryRegionOps>(
          info->ops[index].read_only_memory_region_ops,
          info->ops[index].read_only_memory_region_ops_size);

  // Step 2: Initialize the opaque filesystem structure
  auto filesystem = std::make_unique<TF_Filesystem>();
  TF_Status* c_status = TF_NewStatus();
  Status status = OkStatus();
  core_filesystem_ops->init(filesystem.get(), c_status);
  status = Status(c_status->status);

// does various checks while doing so. `input_nodes` will contain the same
// information as input_tensors just in a different structure to make
// following processing easier. TODO(iga): Simplify this nested structure.
Status ProcessInputs(
    const TF_Graph* fn_body, const char* fn_name, int ninputs,
    const TF_Output* inputs, std::vector<OutputTensor>* input_tensors,

-#define	UT_HOSTSIZE	16
+#define UT_LINESIZE	32
+#define UT_NAMESIZE	32
+#define UT_HOSTSIZE	256
 
 
+/* The structure describing an entry in the database of
+   previous logins.  */
 struct lastlog
   {
     time_t ll_time;
@@ -36,12 +38,16 @@ struct lastlog
     char ll_host[UT_HOSTSIZE];
   };
 
+/* The structure describing an entry in the user accounting database.  */
 struct utmp
   {
-    char ut_line[UT_LINESIZE];

  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Un-pack the parallel tensor to verify that the operation was
  // successful. The resulting structure should be:
  //   second_device{first_device{1. * 3., 2. * 3.}, 3. * 3.}.
  std::array<TensorHandlePtr, 2> second_components;
  ExtractPerDeviceValues(context.get(), multiply_result.get(),

#ifndef TENSORFLOW_C_EAGER_TAPE_H_
#define TENSORFLOW_C_EAGER_TAPE_H_

// Language-agnostic gradient tape. Does not perform backpropagation, just
// maintains the data structures required to do so.

#include <stack>
#include <unordered_map>
#include <unordered_set>
#include <vector>

#include "tensorflow/core/config/flag_defs.h"
#include "tensorflow/core/config/flags.h"