)

load("@python//:defs.bzl", "interpreter")
load("@rules_python//python:pip.bzl", "package_annotation", "pip_parse")

NUMPY_ANNOTATIONS = {
    "numpy": package_annotation(
        additive_build_content = """\
filegroup(
    name = "includes",
    srcs = glob(["site-packages/numpy/core/include/**/*.h"]),
)
cc_library(
    name = "numpy_headers",
    hdrs = [":includes"],

        "//tensorflow/c/eager:c_api_experimental",
        "//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,

# through the docker build command. If the licensing agreement changes, this 
# will need to be updated as well.
RUN New-Item -ItemType Directory -Path C:\Android\android-sdk\licenses; \
    Set-Content -Path .\Android\android-sdk\licenses\android-sdk-license -Value "`n24333f8a63b6825ea9c5514f83c2829b004d1fee" -NoNewLine;

# Add sdkmanager to PATH
RUN $old_path = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\"); \

TFCI_MACOS_CROSS_COMPILE_ENABLE=1
TFCI_MACOS_CROSS_COMPILE_SDK_DEST="tensorflow/tools/toolchains/cross_compile/cc/MacOSX.sdk"
TFCI_MACOS_CROSS_COMPILE_SDK_SOURCE="/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk"

        ":checkpoint_reader",
        ":tf_buffer",
        ":tf_buffer_internal",
        "//tensorflow/c/eager:c_api",
        "//tensorflow/c/eager:c_api_internal",
        "//tensorflow/c/eager:tfe_context_internal",
        "//tensorflow/c/eager:tfe_op_internal",
        "//tensorflow/c/eager:tfe_tensorhandle_internal",
        "//tensorflow/compiler/jit:flags",
        "//tensorflow/compiler/jit:get_compiler_ir",

debugging information with `--config=dbg`, as issues occur on Linux if
there is too much debug info (see [this GitHub
issue](https://github.com/tensorflow/tensorflow/issues/48919) for context). If
you want to debug a kernel, you can compile specific files with `-g` using the
`--per_file_copt` bazel option. For example, if you want to debug the Identity
op, which are in files starting with `identity_op`, you can run

        "//tensorflow/core:protos_all_cc",
    ],
)

cc_library(
    name = "immediate_execution_context",
    hdrs = ["immediate_execution_context.h"],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_context",
        ":immediate_execution_distributed_manager",
        ":immediate_execution_operation",
        ":immediate_execution_tensor_handle",

    *   It is available for immediate use.
        *   See the `TF_RUN_EAGER_OP_AS_FUNCTION` environment variable in
            [eager context](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/eager/context.py).
        *   Eager performance should be similar with this feature enabled.
            *   A roughly 5us per-op overhead may be observed when running many

tenant isolation, resource allocation, model sharing and hardware attacks.

### Tenant isolation

Since any tenants or users providing models, graphs or checkpoints can execute
code in context of the TensorFlow service, it is important to design isolation
mechanisms that prevent unwanted access to the data from other tenants.

Network isolation between different models is also important not only to prevent

        "//tensorflow/c/eager:c_api_experimental",
        "//tensorflow/c/eager:c_api_internal",
        "//tensorflow/c/eager:dlpack",
        "//tensorflow/c/eager:tape",
        "//tensorflow/c/eager:tfe_context_internal",
        "//tensorflow/c/eager:tfe_op_internal",
        "//tensorflow/c/eager:tfe_tensorhandle_internal",
        "//tensorflow/c/experimental/gradients",
        "//tensorflow/c/experimental/gradients/tape",