# use the wheel's TensorFlow installation instead of the one made available
# through bazel. This must be done in a different root directory, //bazel_pip/...,
# because "import tensorflow" run from the root directory would instead import
# the folder instead of the venv package.
# 
# Pass --config=pip to run the same suite of tests. If you want to run just one
# test for investigation, you'll need --config=pip_venv instead, and then you

  }
  return absl::OkStatus();
}

// TODO(agarwal): use an automatic mechanism for handling None arguments to
// gradient functions.
//
// Some gradient functions can accept None arguments for gradients. The
// following maps the operation name to the indices at which the corresponding
// gradient function can accept None values. e.g. FusedBatchNorm outputs 5
// values and hence receives 5 gradient values during backprop. However the

  TFE_DeleteContextOptions(opts);

  TFE_Op* non_comm_op = TFE_NewOp(ctx, "TestNonCommUnavailable", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_TensorHandle* retval[1] = {};
  int num_retvals = 1;
  TFE_Execute(non_comm_op, retval, &num_retvals, status);
  EXPECT_EQ(TF_INTERNAL, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteOp(non_comm_op);

    url_lines = result_store_links.setdefault(url, {})
    # Each bazel RBE invocation should produce two
    # 'Streaming build results to: ...' lines, one at the start, and one at the
    # end of the invocation.
    # If there's a failure message, it will be found in-between these two.

    if not current_url:
      url_lines['start'] = i
    elif current_url == url:
      url_lines['end'] = i
    else:

# simple way to parameterize this from bazel, hence this helper to
# "normalize" the srcjar layout.
#
# Arguments:
#   src_jar - path to the original srcjar
#   dest_jar - path to the destination
# Returns:
#   None
function cp_normalized_srcjar() {
  src_jar="$1"
  dest_jar="$2"
  tmp_dir=$(mktemp -d)
  cp "${src_jar}" "${tmp_dir}/orig.jar"
  pushd "${tmp_dir}"
  # Extract any src/ files
  jar -xf "${tmp_dir}/orig.jar" src/

**Raspberry Pi 2 and 3**      | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi23-py3.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi23-py3.html)           | [Py3](https://storage.googleapis.com/tensorflow-nightly/tensorflow-1.10.0-cp34-none-linux_armv7l.whl)

        data, or by relaxing `Input` shape assumptions (note that you can pass
        shapes with `None` entries for axes that are meant to be dynamic). You
        can also disable the input checking entirely by setting
        `model.input_spec = None`.
    *   Several changes have been made to
        `tf.keras.mixed_precision.experimental`. Note that it is now recommended
        to use the non-experimental `tf.keras.mixed_precision` API.

      // added since the last TF_SessionRun() call.
      for (auto id = session->last_num_graph_nodes; id < num_nodes; ++id) {
        Node* const node = graph.FindNodeId(id);
        if (node != nullptr && node->IsOp()) {
          NodeDef* const node_def = graph_def.add_node();
          *node_def = node->def();
        }
      }
      *graph_def.mutable_library() = graph.flib_def().ToProto();

/// operation tables as read-only. Once a plugin is loaded, none of these
/// operation pointers may change.
///
/// There are 4 function tables: one for each of the 3 file objects in
/// TensorFlow (i.e., `RandomAccessFile`, `WritableFile`,
/// `ReadOnlyMemoryRegion`) and one for all the operations a `Filesystem`
/// implements. Each of them is in a 1-to-1 correspondence with the wrapper

# google internal build on configurable attributes.
#
# For non-configurable distinction between OSS and Google builds, see
# `if_oss()` and `if_google()` macros in tensorflow.bzl.
config_setting(
    name = "oss",
    flag_values = {":oss_setting": "True"},
    visibility = ["//visibility:public"],
)

# Non-configurable setting to indicate open source build.
bool_setting(
    name = "oss_setting",