set -u
  echo '==TFCI==: Evaluated the following TFCI variables from $TFCI:'
  export -p | grep TFCI

  # Load those stored pre-existing TFCI_ vars, if any
  if [[ -s "$FROM_ENV" ]]; then
    echo '==TFCI==: NOTE: Loading the following env parameters, which were'
    echo 'already set in the shell environment. If you want to disable this'
    echo 'behavior, create a new shell.'
    cat "$FROM_ENV"
    source "$FROM_ENV"
    rm "$FROM_ENV"

        shape_handle, static_cast<DataType>(types[i]));
  }

  ic->set_output_handle_shapes_and_types(output.index, shape_and_type_vec);
}

// Helpers for loading a TensorFlow plugin (a .so file).
Status LoadDynamicLibrary(const char* library_filename, void** result,
                          const void** buf, size_t* len);

TF_CAPI_EXPORT extern void TF_JoinThread(TF_Thread* thread);

// \brief Load a dynamic library.
//
// Pass "library_filename" to a platform-specific mechanism for dynamically
// loading a library. The rules for determining the exact location of the
// library are platform-specific and are not documented here.
//
// On success, place OK in status and return the newly created library handle.

// Returns the device of the operation that produced `h`. If `h` was produced by
// a copy, returns the destination device of the copy. Note that the returned
// device name is not always the device holding the tensor handle's memory. If
// you want the latter, use TFE_TensorHandleBackingDeviceName. This function
// will block till the operation that produces `h` has completed.

// Load the library specified by library_filename and register the ops and
// kernels present in that library.
//
// Pass "library_filename" to a platform-specific mechanism for dynamically
// loading a library. The rules for determining the exact location of the
// library are platform-specific and are not documented here.
//
// On success, place OK in status and return the newly created library handle.

### Saved graphs and checkpoints

When loading untrusted serialized computation graphs (in form of a `GraphDef`,
`SavedModel`, or equivalent on-disk format), the set of computation primitives
available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.

The risk of loading untrusted checkpoints depends on the code or graph that you

TF_CAPI_EXPORT void TF_SetXlaMinClusterSize(int size);

// Gets/Sets TF/XLA flag for whether(true) or not(false) to disable constant
// folding. This is for testing to ensure that XLA is being tested rather than
// Tensorflow's CPU implementation through constant folding.
TF_CAPI_EXPORT unsigned char TF_GetXlaConstantFoldingDisabled();
TF_CAPI_EXPORT void TF_SetXlaConstantFoldingDisabled(
    unsigned char should_enable);

  EXPECT_TRUE(second_block);
  // Reading the second full block will return 8 bytes, from a cache hit.
  second_block = false;
  TF_EXPECT_OK(ReadCache(&cache, "", block_size, block_size, &out));
  EXPECT_FALSE(second_block);
  EXPECT_EQ(out.size(), file_size - block_size);
}

TEST(RamFileBlockCacheTest, Inconsistent) {
  // Tests the detection of interrupted reads leading to partially filled blocks

#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/test.h"

from tensorflow.python.platform import tf_logging as _logging
from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

if "dev" in __version__:   # pylint: disable=undefined-variable
  _logging.warning("""