//tensorflow/core/lib/lmdb/testdata:lmdb_testdata
//tensorflow/core/kernels/cloud:bigquery_reader_ops
//tensorflow/python:extra_py_tests_deps
//tensorflow/python:mixed_precision
//tensorflow/python:tf_optimizer
//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_ops_2
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata

    EXPECT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

    // Compare that the graphs match.
    GraphDef expected_gdef;
    GraphDef gdef;
    EXPECT_TRUE(GetGraphDef(expected_graph_, &expected_gdef));
    EXPECT_TRUE(GetGraphDef(graph_, &gdef));
    TF_EXPECT_GRAPH_EQ(expected_gdef, gdef);

    // Compare that the output of the gradients of both graphs match.

  TF_EXPECT_OK(ReadCache(&cache, "a", 8, 1, &out));
  EXPECT_EQ(calls, 3);
  // Advance the fake timestamp so that "a" becomes stale via its first block.
  env->SetNowSeconds(now + 2);
  // The pruning thread periodically compares env->NowSeconds() with the oldest
  // block's timestamp to see if it should evict any files. At the current fake
  // timestamp of `now` + 2, file "a" is stale because its first block is stale,

  off_t offset = 0;
  int bytes_transferred = 0;
  int rc = 1;
  // When `sendfile` returns 0 we stop copying and let callers handle this.
  while (offset < size && rc > 0) {
    // Use uint64 for safe compare SSIZE_MAX
    uint64_t chunk = size - offset;
    if (chunk > SSIZE_MAX) chunk = SSIZE_MAX;

    rc = sendfile(dst_fd, src_fd, &offset, static_cast<size_t>(chunk));
    if (rc < 0) return -1;
    bytes_transferred += rc;

//tensorflow/core:image_testdata
//tensorflow/core/kernels/cloud:bigquery_reader_ops
//tensorflow/python:extra_py_tests_deps
//tensorflow/python:mixed_precision
//tensorflow/python:tf_optimizer
//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib

  return ops::Mul(ctx, inputs[0], inputs[1], &outputs[0], "Mul");
}

// TODO(vnvo2409): Add more tests from `python/ops/gradient_checker_v2_test.py`.
// These tests should not be confused with `[*]_grad_test` which compare the
// result of `gradient_checker` and `[*]_grad`. The tests here test the
// functionality of `gradient_checker` by comparing the result with expected
// manual user-provided gradients.
class GradientCheckerTest

-		  ? (strlen (src) < ((size_t) (n))			      \
-		     ? strcat (dest, src)				      \
-		     : strncat (dest, src, n))				      \
-		  : strncat (dest, src, n)))
-# endif
-#endif
-
-
-/* Compare characters of S1 and S2.  */
-#ifndef _HAVE_STRING_ARCH_strcmp
-# if __GNUC_PREREQ (3, 2)
-#  define strcmp(s1, s2) \
-  __extension__								      \
-  ({ size_t __s1_len, __s2_len;						      \

  elif var:
    write_to_bazelrc('build --config=%s' % bazel_config_name)
  environ_cp[var_name] = str(var)


def convert_version_to_int(version):
  """Convert a version number to a integer that can be used to compare.

  Version strings of the form X.YZ and X.Y.Z-xxxxx are supported. The
  'xxxxx' part, for instance 'homebrew' on OS/X, is ignored.

  Args:
    version: a version to be converted

  Returns:

*   When you contribute a new feature to TensorFlow, the maintenance burden is
    (by default) transferred to the TensorFlow team. This means that the benefit
    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in

        information.

*   `tf.lite`:

    *   Removed `lite.OpHint`, `lite.experimental`, and `lite.constant` from 2.0
        API.

*   Tensors are no longer hashable, but instead compare element-wise with `==`
    and `!=`. Use `tf.compat.v1.disable_tensor_equality()` to return to the
    previous behavior.

*   Performing equality operations on Tensors or Variables with incompatible