.hasArg()
                    .build());
            options.addOption(Option.builder(LOG_FILE)
                    .longOpt("log-file")
                    .hasArg()
                    .desc("Log file where all build output will go (disables output color)")
                    .build());
            options.addOption(Option.builder()

between the runs. All the python-agnostic parts of the build cache from the
previous build will be preserved and reused for the subsequent builds.

### Specifying Python dependencies

During bazel build all TensorFlow's Python dependencies are pinned to their
specific versions. This is necessary to ensure reproducibility of the build.
The pinned versions of the full transitive closure of TensorFlow's dependencies

One or more test dependencies are not in the pip package.
If these test dependencies need to be in the TensorFlow pip package, please
add them to //tensorflow/tools/pip_package/BUILD. Otherwise, add the no_pip tag
to the test, or change code_check_full.bats in the SIG Build repository. That's
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools/code_check_full.bats
Here are the affected tests:

        .serialNumber(3L)
        .commonName(server.hostName)
        .build()
    val certificatePinner =
      CertificatePinner.Builder()
        .add(server.hostName, pin(rootCa.certificate))
        .build()
    val handshakeCertificates =
      HandshakeCertificates.Builder()
        .addTrustedCertificate(rootCa.certificate)
        .build()
    val client =
      clientTestRule.newClientBuilder()

					builder.WriteByte(',')
				}
				builder.WriteQuoted(column)
			}
			builder.WriteString(`) `)
		}

		if len(onConflict.TargetWhere.Exprs) > 0 {
			builder.WriteString(" WHERE ")
			onConflict.TargetWhere.Build(builder)
			builder.WriteByte(' ')
		}
	}

	if onConflict.DoNothing {
		builder.WriteString("DO NOTHING")
	} else {
		builder.WriteString("DO UPDATE SET ")
		onConflict.DoUpdates.Build(builder)
	}

  }

  @Test
  fun proxy() {
    server.enqueue(MockResponse.Builder().body("abc").build())
    server.enqueue(MockResponse.Builder().body("def").build())
    val client =
      clientTestRule.newClientBuilder()
        .proxy(socksProxy.proxy())
        .build()
    val request1 = Request.Builder().url(server.url("/")).build()
    val response1 = client.newCall(request1).execute()

              continue;
            }
            builder.add(Range.range(i, lowerType, j, upperType));
          }
        }
      }
    }
    RANGES = builder.build();
  }

  public void testBuilderRejectsEmptyRanges() {
    for (int i = MIN_BOUND; i <= MAX_BOUND; i++) {
      final int ii = i;
      ImmutableRangeMap.Builder<Integer, Integer> builder = ImmutableRangeMap.builder();

    server.enqueue(
      MockResponse.Builder()
        .code(301)
        .addHeader("Location", "/foo")
        .build(),
    )
    server.enqueue(
      MockResponse.Builder()
        .body("ABC")
        .build(),
    )
    val call =
      client.newCall(
        Request.Builder()
          .url(server.url("/"))
          .build(),
      )
    val response = call.execute()

#
#   Bazel will cache your builds in tensorflow/build_output/cache,
#   and will also try using public build cache results to speed up
#   your builds. This usually saves a lot of time, especially when
#   re-running tests. However, note that:
#
#    - New environments like new CUDA versions, changes to manylinux,
#      compilers, etc. can cause undefined behavior such as build failures
#      or tests passing incorrectly.

        id: tf-version
      -
        name: Build and push
        id: docker_build
        uses: docker/build-push-action@4f58ea79222b3b9dc2c8bbdd6debcef730109a75 # v6.9.0
        with:
          push: true
          context: ./tensorflow/tools/tf_sig_build_dockerfiles
          target: devel
          build-args: |
            PYTHON_VERSION=${{ matrix.python-version }}