"tf_tensor.h",
        "tf_tstring.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_internal",
        ":c_api_macros_hdrs",
        ":c_api_no_xla",
        ":tf_attrtype",
        ":tf_buffer",
        ":tf_file_statistics",
        ":tf_status_helper",
        ":tf_status_internal",
        ":tf_tensor_internal",
        ":tf_tstring",

* `tf.ones`, `tf.zeros`, `tf.fill`, `tf.ones_like`, `tf.zeros_like` now take an additional Layout argument that controls the output layout of their results.

# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

load("@//tensorflow:workspace2.bzl", "tf_workspace2")

tf_workspace2()

load("@//tensorflow:workspace1.bzl", "tf_workspace1")

tf_workspace1()

load("@//tensorflow:workspace0.bzl", "tf_workspace0")

#      Ex. rbe        -- Use RBE for faster builds (Googlers only; see below)
#      Ex. no_docker  -- Disable docker on enabled platforms
#    See full examples below for more details on these. Some other modifiers are:
#      Ex. versions_upload -- for TF official release versions
#      Ex. nightly_upload -- for TF nightly official builds; changes version numbers

        "//tensorflow/c:tf_status",
        "//tensorflow/c:tf_status_helper",
        "//tensorflow/core:framework",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core/grappler:grappler_item",
        "//tensorflow/core/grappler/costs:graph_properties",
        "//tensorflow/core/grappler/optimizers:custom_graph_optimizer",
        "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry",

    python3 -m pip install "$TF_WHEEL"
}

@test "TensorFlow is importable" {
    source /tf/venv/bin/activate
    python3 -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
}

# Is this still useful?
@test "TensorFlow has Keras" {
    source /tf/venv/bin/activate
    python3 -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'

    deps = [
        ":filesystem_interface",
        "//tensorflow/c:tf_status_helper",
        "//tensorflow/c:tf_status_internal",
        "//tensorflow/core/platform:env",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:status",
        "@local_tsl//tsl/platform:errors",
    ],
)

# Compliance test for modules and for interface
tf_cc_test(
    name = "modular_filesystem_test",
    size = "small",

load(
    "//tensorflow:tensorflow.bzl",
    "tf_cc_test",
    "tf_copts",
    "tf_cuda_cc_test",
    "tf_cuda_library",
)
load("//tensorflow:tensorflow.default.bzl", "cc_header_only_library", "filegroup")
load(
    "//tensorflow/core/platform:build_config_root.bzl",
    "tf_cuda_tests_tags",
)
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(

# Experimental gcs filesystem plugin.
load("//tensorflow:tensorflow.bzl", "get_win_copts", "tf_cc_shared_object", "tf_cc_test")
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    licenses = ["notice"],
)

# Filesystem implementation for GCS environments
tf_cc_shared_object(
    name = "gcs_filesystem",
    framework_so = [],

2)  Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).

```