test:linux_cpu_pycpp_test_filters --test_tag_filters=-no_oss,-oss_excluded,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only
test:linux_cpu_pycpp_test_filters --build_tag_filters=-no_oss,-oss_excluded,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only
test:linux_cpu_pycpp_test_filters --test_lang_filters=cc,py --test_size_filters=small,medium

  write_to_bazelrc('test:v1 --test_tag_filters=%s' %
                   ','.join(test_and_build_filters + test_only_filters))
  write_to_bazelrc('test:v1 --build_tag_filters=%s' %
                   ','.join(test_and_build_filters))
  write_to_bazelrc(
      'test:v2 --test_tag_filters=%s' %
      ','.join(test_and_build_filters + test_only_filters + ['-v1only']))
  write_to_bazelrc('test:v2 --build_tag_filters=%s' %

    strings to indices.
*   Support restore session from checkpoint files in v2 in
    `contrib/session_bundle`.
*   Added a tf.contrib.image.rotate function for arbitrary angles.
*   Added `tf.contrib.framework.filter_variables` as a convenience function to
    filter lists of variables based on regular expressions.
*   `make_template()` takes an optional `custom_getter_ param`.

issue](https://github.com/tensorflow/tensorflow/issues/48919) for context). If
you want to debug a kernel, you can compile specific files with `-g` using the
`--per_file_copt` bazel option. For example, if you want to debug the Identity
op, which are in files starting with `identity_op`, you can run

```bash

install_deps()

# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

        "//tensorflow/core/platform:blocking_counter",
        "@com_google_absl//absl/strings",
        "@local_xla//xla/tsl/c:tsl_status_internal",
    ],
    alwayslink = 1,
)

exports_files(
    [
        "version_script.lds",
        "exported_symbols.lds",
    ],
    visibility = ["//visibility:public"],
)

filegroup(
    name = "checkpoint_reader_hdrs",
    srcs = [

    get_compat_files(TENSORFLOW_API_INIT_FILES_V1, 1) + get_nested_compat_files([
        1,
        2,
    ])
)

TENSORFLOW_API_INIT_FILES_V1 = (
    TENSORFLOW_API_INIT_FILES_V1 +
    get_compat_files(TENSORFLOW_API_INIT_FILES, 2) +
    get_compat_files(TENSORFLOW_API_INIT_FILES_V1, 1) + get_nested_compat_files([
        1,
        2,
    ])
)
# copybara:comment_end

//  - target: dy in dx/dy
//  - source: dx in dx/dy
//  - tensor: one of the many inputs or outputs of an operation
//
// Below here we do the gradient algorithm. It works as follows:
//
// First we filter the tape to just the subset of operations we want to
// differentiate. In the process of doing so we count how many times each Tensor
// is used as an input to an op (so we know when we're done computing gradients