load("//tensorflow:tensorflow.default.bzl", "filegroup")
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

# Experimental ops. These will eventually be replaced by machine-generated versions.
package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    licenses = ["notice"],
)

cc_library(
    name = "array_ops",
    srcs = [
        "array_ops.cc",
    ],
    hdrs = [
        "array_ops.h",

    [Environment]::SetEnvironmentVariable('HOME', 'C:\Users\ContainerAdministrator\', 'Machine'); \
    [Environment]::SetEnvironmentVariable('HOMEDRIVE', 'C:', 'Machine'); \
    [Environment]::SetEnvironmentVariable('HOMEPATH', '\Users\ContainerAdministrator\', 'Machine'); \
    [Environment]::SetEnvironmentVariable('GOROOT', 'C:\Program Files\Go\', 'Machine'); \

+  __attribute__ ((__alloc_size__ params))
+
 #include <iostream>
 
 using namespace std;
diff --git a/sysdeps/aarch64/dl-machine.h b/sysdeps/aarch64/dl-machine.h
index 185402f..bbdeae0 100644
--- a/sysdeps/aarch64/dl-machine.h
+++ b/sysdeps/aarch64/dl-machine.h
@@ -49,23 +49,11 @@ elf_machine_load_address (void)
   /* To figure out the load address we use the definition that for any symbol:

title: TensorFlow, Large-scale machine learning on heterogeneous systems

build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

{
    "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.",
    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }

        "//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",
        "//tensorflow/core/grappler/inputs:trivial_test_graph_input_yielder",
        "//tensorflow/core/protobuf:error_codes_proto_impl_cc",
    ],

build --linkopt="-lm"
build --linkopt="-Wl,--undefined-version"

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.

researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)