/tensorflow/python/framework/fast_tensor_util.cpp
/tensorflow/lite/gen/**
/tensorflow/lite/tools/make/downloads/**
/tensorflow/lite/tools/make/gen/**
/api_init_files_list.txt
/estimator_api_init_files_list.txt
*.whl
dist
venv/

# Android
.gradle
.idea
*.iml
local.properties
gradleBuild

# iOS
*.pbxproj
*.xcworkspace
/*.podspec
/tensorflow/lite/**/coreml/**/BUILD
/tensorflow/lite/**/ios/BUILD

        "//tensorflow/lite/c:common",
        "//tensorflow/lite/core/api",
        "//tensorflow/lite/delegates/flex:delegate",
        "//tensorflow/lite/kernels/internal:compatibility",
        "//tensorflow/lite/kernels:builtin_ops",
        "//tensorflow/lite/kernels:reference_ops",
        "//tensorflow/compiler/mlir/lite/schema:schema_fbs",
        "//tensorflow/lite/toco/logging:conversion_log_util",

# CUDA WHEEL
test:linux_cuda_wheel_test_filters --test_tag_filters=gpu,requires-gpu,-no_gpu,-no_oss,-tf_tosa,-oss_excluded,-oss_serial,-benchmark-test,-no_cuda11,-no_oss_py310,-no_oss_py313

        construct the layer with LSTM(recurrent_activation='hard_sigmoid') to
        fallback to 1.x behavior.
*   TensorFlow Lite
    *   Move from `tensorflow/contrib/lite` to `tensorflow/lite`.
    *   Add experimental Java API for injecting TensorFlow Lite delegates
    *   Add support for strings in TensorFlow Lite Java API.
*   `tf.contrib`:
    *   Add Apache Ignite Filesystem plugin to support accessing Apache IGFS.

        "@dlpack",
    ],
)

# TODO(karllessard): only used by //tensorflow/core:mobile_srcs_only_runtime
# right now, remove this public rule when no longer needed (it should be
# replaced by TF Lite)
filegroup(
    name = "srcs",
    srcs = glob(
        [
            "*.cc",
            "*.h",
        ],
        exclude = [
            "c_api_experimental.cc",

"""


def remove_header_permalinks(content: str):
    lines: list[str] = []
    for line in content.split("\n"):
        match = header_with_permalink_pattern.match(line)
        if match:
            hashes, title, *_ = match.groups()
            line = f"{hashes} {title}"
        lines.append(line)
    return "\n".join(lines)


def generate_readme_content() -> str:

```

</div>

In the output, there's a line with something like:

```hl_lines="4"
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

That line shows the URL where your app is being served on your local machine.

### Check it { #check-it }

 * into lines. Like {@link BufferedReader}, these methods break lines on any of {@code \n}, {@code
 * \r} or {@code \r\n}, do not include the line separator in each line and do not consider there to
 * be an empty line at the end if the contents are terminated with a line separator.
 *

---

This would be the parallel equivalent story for burgers. 🍔

For a more "real life" example of this, imagine a bank.

Up to recently, most of the banks had multiple cashiers 👨‍💼👨‍💼👨‍💼👨‍💼 and a big line 🕙🕙🕙🕙🕙🕙🕙🕙.

All of the cashiers doing all the work with one client after the other 👨‍💼⏯.

And you have to wait 🕙 in the line for a long time or you lose your turn.

/// info

The important point is that your app will be able to produce each line in turn, while the client consumes the previous lines.

///

/// note | Technical Details