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.

+  __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:

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.

        "//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",
    ],

## 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 a computational framework, primarily for use in machine
# learning applications.

load("@bazel_skylib//lib:selects.bzl", "selects")
load("@bazel_skylib//rules:common_settings.bzl", "bool_flag", "bool_setting")
load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
load(
    "//tensorflow:tensorflow.bzl",
    "VERSION",
    "VERSION_MAJOR",
    "check_deps",
    "if_google",
    "if_oss",

*   `tf.keras.optimizers.Optimizer` now points to the new Keras optimizer, and
    old optimizers have moved to the `tf.keras.optimizers.legacy` namespace.
    If you find your workflow failing due to this change,
    you may be facing one of the following issues:

    *   **Checkpoint loading failure.** The new optimizer handles optimizer
        state differently from the old optimizer, which simplies the logic of

    [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'); \

build:rbe_cross_compile_base --remote_instance_name=projects/tensorflow-testing/instances/default_instance

# XLA depends on some local Python headers that are configured as Genrule. They
# are present on the local host machine but not on the remote execution machine,
# leading to build failures. To resolve the issue, the following line is added
# to make sure all Genrule targets are excuted locally.
build:rbe_cross_compile_base_xla --config=rbe_cross_compile_base

def is_linux():
  return platform.system() == 'Linux'


def is_macos():
  return platform.system() == 'Darwin'


def is_ppc64le():
  return platform.machine() == 'ppc64le'


def is_s390x():
  return platform.machine() == 's390x'


def is_cygwin():
  return platform.system().startswith('CYGWIN_NT')


def get_input(question):
  try:
    try:
      answer = raw_input(question)