"//tensorflow/c/eager:tfe_context_internal",
        "//tensorflow/core:framework",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/common_runtime/eager:context",
    ],
)

cc_library(
    name = "parallel_device_testlib",
    testonly = 1,
    srcs = ["parallel_device_testlib.cc"],

build --distinct_host_configuration=false

# Store performance profiling log in the mounted artifact directory.
# The profile can be viewed by visiting chrome://tracing in a Chrome browser.
# See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling
build --profile=/tf/pkg/profile.json.gz

# Use the rebuilt gcc toolchain to compile for manylinux2014
build --crosstool_top="@ml2014_aarch64_config_aarch64//crosstool:toolchain"

        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
    ],
)

tf_cc_test(
    name = "while_loop_test",
    size = "medium",
    srcs = ["while_loop_test.cc"],
    deps = [
        ":c_api",
        ":c_test_util",
        "//tensorflow/core:lib",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
    ],
)

tf_custom_op_library(

    visibility = ["//visibility:public"],
    deps = [
        "//tensorflow/c:tf_file_statistics",
        "//tensorflow/c:tf_status",
    ],
)

# Core TensorFlow depends on this, will be included in main library
cc_library(
    name = "modular_filesystem",
    srcs = [
        "modular_filesystem.cc",
        "modular_filesystem_registration.cc",
    ],
    hdrs = [
        "modular_filesystem.h",

        ":grappler",
        "//tensorflow/c:c_api_internal",
        "//tensorflow/c:tf_buffer_internal",
        "//tensorflow/core:framework",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/grappler:grappler_item",
        "//tensorflow/core/grappler/clusters:single_machine",
        "//tensorflow/core/grappler/costs:graph_properties",

***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository.

### Contributing code

If you have improvements to TensorFlow, send us your pull requests! For those
just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

        "//tensorflow/compiler/mlir/tensorflow/c:mlir_c_api_registration",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/lib/llvm_rtti",
        "@com_google_absl//absl/container:flat_hash_set",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/types:span",
    ],
)

    TensorFlow models, please demonstrate an exploit against one of Alphabet's
    models in <https://tfhub.dev/>
  - An explanation of who can exploit this vulnerability, and what they gain
    when doing so. Write an attack scenario that demonstrates how your issue
    violates the use cases and security assumptions defined in the threat model.

build --copt=-Wno-gnu-offsetof-extensions

# Store performance profiling log in the mounted artifact directory.
# The profile can be viewed by visiting chrome://tracing in a Chrome browser.
# See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling
build --profile=/tf/pkg/profile.json.gz

# Use the rebuilt gcc toolchain to compile for manylinux2014
build --crosstool_top="@ml2014_clang_aarch64_config_aarch64//crosstool:toolchain"

    of other integer types) in `tf.constant`.
*   Make the `gain` argument of convolutional orthogonal initializers
    (`convolutional_delta_orthogonal`, `convolutional_orthogonal_1D`,
    `convolutional_orthogonal_2D`, `convolutional_orthogonal_3D`) have
    consistent behavior with the `tf.initializers.orthogonal` initializer, i.e.
    scale the output l2-norm by `gain` and NOT by `sqrt(gain)`. (Note that these