code: def distribution(
      series: List[Union[np.ndarray, "torch.Tensor"]],
      step: np.ndarray,
      *,
      names: Optional[List[str]] = None,
      levels: int = 5,
      alpha: int = 0.6,
      color_scheme: str = "tableau10",
      height: int = 400,
      width: int = 800,
      height_minimap: int = 100,
  ):
      ...
  
imports: from typing import Tuple, Optional, List, overload, Union, TYPE_CHECKING
  import numpy
  from labml.internal.analytics import cache
  from labml.internal.analytics.altair import density
  from labml.internal.analytics.altair import histogram
  from labml.internal.analytics.altair import scatter
  from labml.internal.analytics.altair import binned_heatmap
  from labml.internal.analytics.indicators import IndicatorCollection
  from labml.internal.analytics.models import ModelProbe
  from labml.internal.analytics.models import ValueCollection
  from labml.internal.analytics.models import DeepValueCollection
  from labml.internal.analytics.viz import text_attention
  from labml.internal.analytics.viz import init_inline_viz
label_desc: Use image node_id 9 for calling a global function with example usage: distribution(series, step) without return types