disable the sharding so that all the test outputs are in one file. However,
    it may slow down the tests for not running in parallel and may cause the
    test to timeout but it could be useful when you need to execute a single
    test or more in general your filtered/selected tests have a very low
    execution time and the sharding

        "//tensorflow/cc/saved_model:tag_constants",
        "//tensorflow/compiler/jit",
        "//tensorflow/core:array_ops_op_lib",
        "//tensorflow/core:bitwise_ops_op_lib",
        "//tensorflow/core:control_flow_ops_op_lib",
        "//tensorflow/core:core_cpu_internal",
        "//tensorflow/core:direct_session",
        "//tensorflow/core:framework",
        "//tensorflow/core:framework_internal",

*   EagerTensor now supports numpy buffer interface for tensors.
*   Add toggles `tf.enable_control_flow_v2()` and `tf.disable_control_flow_v2()`
    for enabling/disabling v2 control flow.
*   Enable v2 control flow as part of `tf.enable_v2_behavior()` and
    `TF2_BEHAVIOR=1`.
*   AutoGraph translates Python control flow into TensorFlow expressions,
    allowing users to write regular Python inside `tf.function`-decorated

functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack
scenario would require a malicious user to be able to control the model which as
stated above is already equivalent to code execution. In any case, the

# ARM64 PYCPP
# In Linux Arm64 presubmit/continuous build, we cross-compile the binaries on
# Linux x86 so that we can use RBE. Since tests still need to run on the single
# host Arm64 machine, the build becomes too slow (~30 min) to be a presubmit.
# For testing purposes, we want to see the runtime performance of an
# experimental job that is build-only, i.e, we only build the test targets and