code: def histogram(
      series: Union[np.ndarray, "torch.Tensor"],
      *,
      low: Optional[float] = None,
      high: Optional[float] = None,
      height: int = 400,
      width: int = 800,
      height_minimap: int = 100,
  ):
      r"""
      Creates a histogram with Altair
  
      Arguments:
          series(Union[np.ndarray, torch.Tensor]): Data
  
      Keyword Arguments:
          low: values less than this are ignored
          high: values greater than this are ignored
          height: height of the visualization
          width: width of the visualization
          height_minimap: height of the view finder
  
      Return:
          The Altair visualization
      """
  
      table = _histogram.data_to_table(series, low, high)
  
      return _histogram.render(
          table,
          width=width,
          height=height,
          height_minimap=height_minimap,
      )
  
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 12 for calling a global function with example usage: histogram(series) and returns: _histogram