code: def parse_lines_inputs(
      x=None,
      y=None,
      samples=None,
      ids=None,
      link_field=None,
      labels=None,
      sizes=None,
  ):
      if y is None:
          raise ValueError("You must provide 'y' values")
  
      raw_y_values = not (etau.is_str(y) or _is_expr(y))
  
      if raw_y_values:
          is_frames = y and etau.is_container(y[0])
      elif samples is not None and samples._contains_videos():
          is_frames = foe.is_frames_expr(y)
      else:
          is_frames = False
  
      if is_frames and link_field is None:
          link_field = "frames"
  
      if ids is None and samples is not None:
          ref = y if raw_y_values else None
          ids = _get_ids(
              samples,
              link_field=link_field,
              ref=ref,
              is_frames=is_frames,
          )
  
      x = _parse_values(
          x,
          "x",
          samples=samples,
          ids=ids,
          link_field=link_field,
          is_frames=is_frames,
      )
  
      y = _parse_values(
          y,
          "y",
          samples=samples,
          ids=ids,
          link_field=link_field,
          is_frames=is_frames,
      )
  
      sizes = _parse_values(
          sizes,
          "sizes",
          samples=samples,
          ids=ids,
          link_field=link_field,
          ref=y,
          is_frames=is_frames,
      )
  
      if x is None:
          if is_frames:
              x = [np.arange(1, len(yi) + 1) for yi in y]
          else:
              x = np.arange(1, len(y) + 1)
  
      if is_frames and x and not etau.is_container(x[0]):
          x = [x] * len(y)
  
      if is_frames:
          if sizes is None:
              sizes = [None] * len(y)
  
          if ids is None:
              ids = [None] * len(y)
  
          if labels is None:
              labels = [str(i) for i in range(1, len(y) + 1)]
          else:
              labels = _parse_values(labels, "labels", samples=samples)
      else:
          x = [x]
          y = [y]
          ids = [ids]
          sizes = [sizes]
          labels = [labels]
  
      return x, y, ids, link_field, labels, sizes, is_frames
  
imports: import itertools
  import warnings
  import numpy
  import sklearn.linear_model
  import sklearn.metrics
  import eta.core.serial
  import eta.core.utils
  import fiftyone.core.expressions
  import fiftyone.core.labels
label_desc: Use image node_id 3 for calling a global function with example usage: parse_lines_inputs(x, y, samples, ids, link_field, labels, sizes) and returns: x,  y,  ids,  link_field,  labels,  sizes,  is_frames