code: def define_flags():
      """Define command line flags."""
      flags = tf.app.flags
  
      # Dataset Parameters
      flags.DEFINE_enum(
          "dataset",
          "amass",
          list(k for k in datasets.dataset_dict.keys()),
          "Name of the dataset.",
      )
      flags.DEFINE_string(
          "data_dir", None, "Directory to load data from."
      )
      flags.mark_flag_as_required("data_dir")
      flags.DEFINE_integer(
          "sample_bbox", 1024, "Number of bbox samples."
      )
      flags.DEFINE_integer(
          "sample_surf", 1024, "Number of surface samples."
      )
      flags.DEFINE_integer("batch_size", 12, "Batch size.")
      flags.DEFINE_integer(
          "motion", 0, "Index of the motion for evaluation."
      )
      flags.DEFINE_integer(
          "subject", 0, "Index of the subject for training."
      )
  
      # Model Parameters
      flags.DEFINE_enum(
          "model",
          "nasa",
          list(k for k in models.model_dict.keys()),
          "Name of the model.",
      )
      flags.DEFINE_integer("n_parts", 24, "Number of parts.")
      flags.DEFINE_integer(
          "total_dim", 960, "Dimension of the latent vector (in total)."
      )
      flags.DEFINE_bool(
          "shared_decoder", False, "Whether to use shared decoder."
      )
      flags.DEFINE_float(
          "soft_blend", 5.0, "The constant to blend parts."
      )
      flags.DEFINE_bool(
          "projection", True, "Whether to use projected shape features."
      )
      flags.DEFINE_float(
          "level_set", 0.5, "The value of the level_set."
      )
      flags.DEFINE_integer(
          "n_dims", 3, "The dimension of the query points."
      )
  
      # Training Parameters
      flags.DEFINE_float("lr", 1e-4, "Learning rate")
      flags.DEFINE_string("train_dir", None, "Training directory.")
      flags.mark_flag_as_required("train_dir")
      flags.DEFINE_integer(
          "max_steps", 200000, "Number of optimization steps."
      )
      flags.DEFINE_integer(
          "save_every", 5000, "Number of steps to save checkpoint."
      )
      flags.DEFINE_integer(
          "summary_every", 500, "Number of steps to save checkpoint."
      )
      flags.DEFINE_float(
          "label_w", 0.5, "Weight of labed vertices loss."
      )
      flags.DEFINE_float("minimal_w", 0.05, "Weight of minimal loss.")
      flags.DEFINE_bool(
          "use_vert",
          True,
          "Whether to use vertices on the mesh for training.",
      )
      flags.DEFINE_bool(
          "use_joint",
          True,
          "Whether to use joint-based transformation.",
      )
      flags.DEFINE_integer(
          "sample_vert", 2048, "Number of vertex samples."
      )
  
      # Evalulation Parameters
      flags.DEFINE_bool(
          "gen_mesh_only", False, "Whether to generate meshes only."
      )
  
      # Tracking Parameters
      flags.DEFINE_float("theta_lr", 5e-4, "Learning rate")
      flags.DEFINE_integer(
          "max_steps_per_frame",
          1792,
          "Number of optimization steps for tracking each frame.",
      )
      flags.DEFINE_enum(
          "gradient_type",
          "reparam",
          ["vanilla", "reparam"],
          "Type of gradient to use in theta optimization.",
      )
      flags.DEFINE_integer(
          "sample_track_vert",
          1024,
          "Number of vertex samples for tracking each frame.",
      )
      flags.DEFINE_integer(
          "n_noisy_samples", 8, "Number of noisy samples per vertex"
      )
      flags.DEFINE_float(
          "bandwidth", 1e-2, "Bandwidth of the gaussian noises."
      )
      flags.DEFINE_bool(
          "left_trans",
          False,
          "Whether to use left side transformation (True) or right side (False).",
      )
      flags.DEFINE_string(
          "joint_data", None, "Path to load joint data."
      )
      flags.DEFINE_float(
          "glue_w", 20.0, "Weight of length constraint loss."
      )
      flags.DEFINE_float(
          "trans_range", 1.0, "The range of allowed translations."
      )
  
imports: from os import path
  import numpy
  from skimage import measure
  import tensorflow.compat.v1
  from tensorflow_graphics.projects.cvxnet.lib.libmise import mise
  from tensorflow_graphics.projects.nasa.lib import datasets
  from tensorflow_graphics.projects.nasa.lib import models
  import tensorflow_probability
  from tqdm import trange
  import trimesh
label_desc: Use image node_id 1 for calling a global function with example usage: define_flags() without return types