build:cross_compile_linux_arm64 --crosstool_top=//tensorflow/tools/toolchains/cross_compile/cc:cross_compile_toolchain_suite

# XLA uses different paths for platforms and crosstool_top.
build:cross_compile_linux_arm64_xla --config=cross_compile_base_xla
build:cross_compile_linux_arm64_xla --platforms=//tools/toolchains/cross_compile/config:linux_aarch64

    all compiler warnings in a file, etc.)
*   Tests should follow the
    [testing best practices](https://www.tensorflow.org/community/contribute/tests)
    guide.

#### License

Include a license at the top of new files.

*   [C/C++ license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/op.cc#L1)

# 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"

build --copt="-mtune=generic" --copt="-march=armv8-a" --copt="-O3"

# Test-related settings below this point.

    absl::Span<const tensorflow::DataType> dtypes) {
  if (call_state_.top().backward_tape != nullptr) {
    // If we're forwarding Accumulate calls to backward_tape's RecordOperation,
    // we should also delegate ShouldRecord.
    return call_state_.top().backward_tape->ShouldRecord(tensor_ids, dtypes);
  }
  if (call_state_.top().accumulating) {
    return false;
  }
  for (int i = 0; i < tensor_ids.size(); ++i) {

"""

import logging
import unittest
import pkg_resources

logging.basicConfig(level=logging.INFO)


class ImportApiPackagesTest(unittest.TestCase):
  """ImportApiPackagesTest class. See description at the top of the file."""

  def setUp(self):
    def _get_api_packages_v2():
      api_packages_path = pkg_resources.resource_filename(
          "tensorflow", "_api/v2/api_packages.txt"
      )

  // Returns whether the runtime is backed by TFRT or the legacy TF Eager
  // Runtime. This is necessary to decouple runtime-dependent
  // code that is layered on top of the runtime.
  virtual bool UsesTFRT() = 0;

  // List attributes of available devices
  virtual void ListDevices(std::vector<DeviceAttributes>* devices) = 0;

    AbstractTensorHandlePtr fMinus(f_outputs[0]);

    // Take Difference of both estimates: (f(theta + eps) - f(theta - eps)).
    TF_RETURN_IF_ERROR(
        ops::Sub(ctx, fPlus.get(), fMinus.get(), f_outputs, "sub_top"));
    AbstractTensorHandlePtr fDiff(f_outputs[0]);

    // Calculate using the difference quotient definition:
    // (f(theta + eps) - f(theta - eps)) / (2 * eps).
    TF_RETURN_IF_ERROR(

import glob
import json
import os
import platform
import re
import subprocess
import sys

# pylint: disable=g-import-not-at-top
try:
  from shutil import which
except ImportError:
  from distutils.spawn import find_executable as which
# pylint: enable=g-import-not-at-top

_DEFAULT_CUDA_VERSION = '11'
_DEFAULT_CUDNN_VERSION = '2'
_DEFAULT_TENSORRT_VERSION = '6'
_DEFAULT_CUDA_COMPUTE_CAPABILITIES = '3.5,7.0'

#include "tensorflow/core/platform/thread_annotations.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/public/version.h"

// The implementation below is at the top level instead of the
// brain namespace because we are defining 'extern "C"' functions.
using tensorflow::AttrValueMap;
using tensorflow::DataType;
using tensorflow::ExtendSessionGraphHelper;
using tensorflow::FullTypeDef;

for an example.

*  TensorFlow IO support is now available for Apple Silicon packages.

*  Refactor CpuExecutable to propagate LLVM errors.

## Keras

Keras is a framework built on top of the TensorFlow. See more details on the Keras [website](https://keras.io/).

### Major Features and Improvements

* `tf.keras`