import java.util.List;
import java.util.Objects;
import java.util.Optional;

import org.apache.maven.api.RemoteRepository;
import org.apache.maven.api.Session;
import org.apache.maven.api.annotations.Experimental;
import org.apache.maven.api.annotations.Immutable;
import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.annotations.NotThreadSafe;
import org.apache.maven.api.annotations.Nullable;

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/c_api_unified_experimental.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/platform/tensor_float_32_utils.h"
#include "tensorflow/core/platform/test.h"

#     verbose_logs:     Show all compiler warnings during build.
#     monolithic:       Build all TF C++ code into a single shared object.
#     dynamic_kernels:  Try to link all kernels dynamically (experimental).
#     dbg:              Build with debug info
#
# TF version options;
#     v2: Build TF v2
#
# Feature and Third party library support options:
#     xla:          Build TF with XLA

import java.util.List;
import java.util.Map;
import java.util.Objects;

import org.apache.maven.api.RemoteRepository;
import org.apache.maven.api.Session;
import org.apache.maven.api.annotations.Experimental;
import org.apache.maven.api.annotations.Immutable;
import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.annotations.NotThreadSafe;
import org.apache.maven.api.annotations.Nullable;

"""Experimental Bzlmod support for TensorFlow"""

module(
    name = "tensorflow",
    repo_name = "org_tensorflow",
)

bazel_dep(name = "bazel_features", version = "1.36.0")
bazel_dep(name = "abseil-cpp", version = "20250814.1", repo_name = "com_google_absl")
single_version_override(
    module_name = "abseil-cpp",
    patch_strip = 1,
    patches = [
        "//third_party/absl:build_dll.patch",
        "//third_party/absl:endian.patch",

#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/tape.h"
#include "tensorflow/core/common_runtime/eager/attr_builder.h"

namespace tensorflow {
namespace gradients {

// =============== Experimental C++ API for computing gradients ===============

// Sample gradient function:
//
// class AddGradientFunction : public GradientFunction {
//  public:
//   Status Compute(Context* ctx,

  - [Downloads for v1.2.0](#downloads-for-v120)
  - [Changes since v1.1.1](#changes-since-v111)
    - [Major Themes](#major-themes)
    - [Other notable improvements](#other-notable-improvements)
    - [Experimental Features](#experimental-features)
    - [Action required](#action-required-1)
    - [Known Issues](#known-issues)
      - [Docker Known Issues](#docker-known-issues)
        - [1.9.1](#191)

    - [Other notable changes](#other-notable-changes-10)
- [v1.3.0-beta.2](#v130-beta2)
  - [Downloads](#downloads-11)
  - [Changes since v1.3.0-beta.1](#changes-since-v130-beta1)
    - [Experimental Features](#experimental-features)
    - [Other notable changes](#other-notable-changes-11)
- [v1.3.0-beta.1](#v130-beta1)
  - [Downloads](#downloads-12)
  - [Changes since v1.3.0-alpha.5](#changes-since-v130-alpha5)

import org.apache.maven.api.RemoteRepository;
import org.apache.maven.api.Service;
import org.apache.maven.api.Session;
import org.apache.maven.api.SourceRoot;
import org.apache.maven.api.annotations.Experimental;
import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.annotations.Nullable;

/**
 * Interface to manage the project state and artifacts during the Maven build lifecycle.

- `MINIO_DOMAIN` environment variable should be defined and exported for bucket DNS style support.
- Running Distributed MinIO on **Windows** operating system is considered **experimental**. Please proceed with caution.

Example 1: Start distributed MinIO instance on n nodes with m drives each mounted at `/export1` to `/exportm` (pictured below), by running this command on all the n nodes: