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-# TensorFlow Lite Converter command-line glossary
-
-This page is complete reference of command-line flags used by the TensorFlow
-Lite Converter's command line starting from TensorFlow 1.9 up until the most
-recent build of TensorFlow. It is complemented by the following other documents:
-
-*   [README](../README.md)
-*   [Command-line examples](cmdline_examples.md)
-*   [Python API examples](python_api.md)
-
-Table of contents:
-
-*   [High-level flags](#high-level-flags)
-*   [Model flags](#model-flags)
-*   [Transformation flags](#transformation-flags)
-*   [Logging flags](#logging-flags)
-
-## High-level flags
-
-The following high level flags specify the details of the input and output
-files. The flag `--output_file` is always required. Additionally, either
-`--graph_def_file`, `--saved_model_dir` or `--keras_model_file` is required.
-
-*   `--output_file`. Type: string. Specifies the full path of the output file.
-*   `--graph_def_file`. Type: string. Specifies the full path of the input
-    GraphDef file frozen using
-    [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py).
-*   `--saved_model_dir`. Type: string. Specifies the full path to the directory
-    containing the SavedModel.
-*   `--keras_model_file`. Type: string. Specifies the full path of the HDF5 file
-    containing the tf.keras model.
-*   `--output_format`. Type: string. Default: `TFLITE`. Specifies the format of
-    the output file. Allowed values:
-    *   `TFLITE`: TensorFlow Lite FlatBuffer format.
-    *   `GRAPHVIZ_DOT`: GraphViz `.dot` format containg a visualization of the
-        graph after graph transformations.
-        *   Note that passing `GRAPHVIZ_DOT` to `--output_format` leads to loss
-            of TFLite specific transformations. Therefore, the resulting
-            visualization may not reflect the final set of graph
-            transformations. To get a final visualization with all graph
-            transformations use `--dump_graphviz_dir` instead.
-
-The following flags specify optional parameters when using SavedModels.
-
-*   `--saved_model_tag_set`. Type: string. Default:
-    [kSavedModelTagServe](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/cc/saved_model/tag_constants.h).
-    Specifies a comma-separated set of tags identifying the MetaGraphDef within
-    the SavedModel to analyze. All tags in the tag set must be specified.
-*   `--saved_model_signature_key`. Type: string. Default:
-    [DEFAULT_SERVING_SIGNATURE_DEF_KEY](https://www.tensorflow.org/api_docs/python/tf/saved_model/signature_constants).
-    Specifies the key identifying the SignatureDef containing inputs and
-    outputs.
-
-## Model flags
-
-*Model flags* provide additional information about the model stored in the input
-file.
-
-*   `--input_arrays`. Type: comma-separated list of strings. Specifies the list
-    of names of input activation tensors.
-*   `--output_arrays`. Type: comma-separated list of strings. Specifies the list
-    of names of output activation tensors.
-
-The following flags define properties of the input tensors. Each item in the
-`--input_arrays` flag should correspond to each item in the following flags
-based on index.
-
-*   `--input_shapes`. Type: colon-separated list of comma-separated lists of
-    integers. Each comma-separated list of integers gives the shape of one of
-    the input arrays specified in
-    [TensorFlow convention](https://www.tensorflow.org/versions/r1.2/programmers_guide/dims_types#shape).
-    *   Example: `--input_shapes=1,60,80,3` for a typical vision model means a
-        batch size of 1, an input image height of 60, an input image width of
-        80, and an input image depth of 3 (representing RGB channels).
-    *   Example: `--input_arrays=foo,bar --input_shapes=2,3:4,5,6` means "foo"
-        has a shape of [2, 3] and "bar" has a shape of [4, 5, 6].
-*   `--std_dev_values`, `--mean_values`. Type: comma-separated list of floats.
-    These specify the (de-)quantization parameters of the input array, when it
-    is quantized. This is only needed if `inference_input_type` is
-    `QUANTIZED_UINT8`.
-    *   The meaning of `mean_values` and `std_dev_values` is as follows: each
-        quantized value in the quantized input array will be interpreted as a
-        mathematical real number (i.e. as an input activation value) according
-        to the following formula:
-        *   `real_value = (quantized_input_value - mean_value) / std_dev_value`.
-    *   When performing float inference (`--inference_type=FLOAT`) on a
-        quantized input, the quantized input would be immediately dequantized by
-        the inference code according to the above formula, before proceeding
-        with float inference.
-    *   When performing quantized inference
-        (`--inference_type=QUANTIZED_UINT8`), no dequantization is performed by
-        the inference code. However, the quantization parameters of all arrays,
-        including those of the input arrays as specified by `mean_value` and
-        `std_dev_value`, determine the fixed-point multipliers used in the
-        quantized inference code. `mean_value` must be an integer when
-        performing quantized inference.
-
-## Transformation flags
-
-*Transformation flags* specify options of the transformations to be applied to
-the graph, i.e. they specify requested properties that the output file should
-have.
-
-*   `--inference_type`. Type: string. Default: `FLOAT`. Data type of all
-    real-number arrays in the output file except for input arrays (defined by
-    `--inference_input_type`). Must be `{FLOAT, QUANTIZED_UINT8}`.
-
-    This flag only impacts real-number arrays including float and quantized
-    arrays. This excludes all other data types including plain integer arrays
-    and string arrays. Specifically:
-
-    *   If `FLOAT`, then real-numbers arrays will be of type float in the output
-        file. If they were quantized in the input file, then they get
-        dequantized.
-    *   If `QUANTIZED_UINT8`, then real-numbers arrays will be quantized as
-        uint8 in the output file. If they were float in the input file, then
-        they get quantized.
-
-*   `--inference_input_type`. Type: string. Data type of a real-number input
-    array in the output file. By default the `--inference_type` is used as type
-    of all of the input arrays. Flag is primarily intended for generating a
-    float-point graph with a quantized input array. A Dequantized operator is
-    added immediately after the input array. Must be `{FLOAT, QUANTIZED_UINT8}`.
-
-    The flag is typically used for vision models taking a bitmap as input but
-    requiring floating-point inference. For such image models, the uint8 input
-    is quantized and the quantization parameters used for such input arrays are
-    their `mean_value` and `std_dev_value` parameters.
-
-*   `--default_ranges_min`, `--default_ranges_max`. Type: floating-point.
-    Default value for the (min, max) range values used for all arrays without a
-    specified range. Allows user to proceed with quantization of non-quantized
-    or incorrectly-quantized input files. These flags produce models with low
-    accuracy. They are intended for easy experimentation with quantization via
-    "dummy quantization".
-
-*   `--drop_control_dependency`. Type: boolean. Default: True. Indicates whether
-    to drop control dependencies silently. This is due to TensorFlow Lite not
-    supporting control dependencies.
-
-*   `--reorder_across_fake_quant`. Type: boolean. Default: False. Indicates
-    whether to reorder FakeQuant nodes in unexpected locations. Used when the
-    location of the FakeQuant nodes is preventing graph transformations
-    necessary to convert the graph. Results in a graph that differs from the
-    quantized training graph, potentially causing differing arithmetic behavior.
-
-*   `--allow_custom_ops`. Type: string. Default: False. Indicates whether to
-    allow custom operations. When false, any unknown operation is an error. When
-    true, custom ops are created for any op that is unknown. The developer will
-    need to provide these to the TensorFlow Lite runtime with a custom resolver.
-
-*   `--post_training_quantize`. Type: boolean. Default: False. Boolean
-    indicating whether to quantize the weights of the converted float model.
-    Model size will be reduced and there will be latency improvements (at the
-    cost of accuracy).
-
-## Logging flags
-
-The following flags generate graph visualizations of the graph as
-[GraphViz](https://www.graphviz.org/) `.dot` files at various points during
-graph transformations:
-
-*   `--dump_graphviz_dir`. Type: string. Specifies the full path of the
-    directory to output GraphViz `.dot` files. Outputs the graph immediately
-    after reading in the graph and after all of the transformations have been
-    completed.
-*   `--dump_graphviz_video`. Type: boolean. Outputs GraphViz after every graph
-    transformation. Requires `--dump_graphviz_dir` to be specified.