However, for the vast majority of issues, we aim to empower individuals to first resolve conflicts themselves, asking for help when needed, and only after that fails to escalate further. This approach gives people more control over the outcome of their dispute. 

If you are experiencing or witnessing conflict, we ask you to use the following escalation strategy to address the conflict:

1.  Address the perceived conflict directly with those involved, preferably in a

    "//tensorflow/java:libtensorflow_jni.so" \
    "//tensorflow/tools/lib_package:jnilicenses_generate"
}

# This test ensures that all the targets built into the Python package include
# their dependencies. It's a rewritten version of the "smoke test", an older
# Python script that was very difficult to understand. See
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/pip_smoke_test.py

        //
        // TODO(allenl): There may be smarter ways to do this copy in some
        // cases, i.e. with a collective broadcast. We'll need to be careful
        // about things that are taken as inputs on the host or on their
        // existing device (for multi-device functions).
        std::unique_ptr<ParallelTensor> parallel_tensor(
            parallel_device.CopyToParallelDevice(

cd "$TFCI_GIT_DIR"

# "TFCI" may optionally be set to the name of an env-type file with TFCI
# variables in it, OR may be left empty if the user has already exported the
# relevant variables in their environment. Because of 'set -o allexport' above
# (which is equivalent to "set -a"), every variable in the file is exported
# for other files to use.
#
# Separately, if TFCI is set *and* there are also additional TFCI_ variables

  if (dev->strict_scope_placement && *requested_placement == '\0') {
    TF_SetStatus(s, TF_INTERNAL,
                 "Ops must be placed on the device explicitly, or their inputs "
                 "first copied to other devices.");
    return;
  }
  TFE_Context* context = TFE_OpGetContext(original_op, s);
  if (TF_GetCode(s) != TF_OK) return;

  return parsed_args


def parse_log(file_path: str,
              verbose: bool = False) -> ResultDictType:
  """Finds ResultStore links, and tries to determine their status."""
  with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
    log_lines = f.read().splitlines()

  result_store_links: ResultDictType = {}
  current_url = None
  for i in range(len(log_lines)):

        tensors_(std::move(tensors)),
        shape_(absl::nullopt),
        dtype_(dtype) {}

  const ParallelDevice& device_;
  std::vector<TensorHandlePtr> tensors_;
  // Parallel tensors are immutable but compute their shape lazily unless it is
  // provided on construction. The optional has a value if the lazy computation
  // has been completed or the shape was provided on construction.
  mutable absl::optional<std::vector<int64_t>> shape_;

  bool HasTensor(const string& name) const;
  const string DebugString() const;

  // Returns a map from variable names to their shapes.  Slices of a partitioned
  // tensor are combined into a single entry.
  const TensorSliceReader::VarToShapeMap& GetVariableToShapeMap() const;

  // Returns a map from variable names to their data types.  Slices of a
  // partitioned tensor are combined into a single entry.

// * int is used as an index into arrays.
// * Deletion functions are safe to call on nullptr.
//
// Questions left to address:
// * Might at some point need a way for callers to provide their own Env.
// * Maybe add TF_TensorShape that encapsulates dimension info.
//
// Design decisions made:
// * Backing store for tensor memory has an associated deallocation

          std::vector<char> x(block_size, 'x');
          EXPECT_EQ(out, x);
        }));
  }
  // The `threads` destructor blocks until the threads can be joined, once their
  // respective reads finish (which happens once they are all concurrently being
  // executed, or 10 seconds have passed).
}

TEST(RamFileBlockCacheTest, CoalesceConcurrentReads) {