parameters: (arr, pad_amt, num, axis=-1)
return_statement: return _do_prepend(arr, min_chunk.repeat(pad_amt, axis), axis=axis)
docstring: Prepend `pad_amt` minimum values along `axis`.
  
      Parameters
      ----------
      arr : ndarray
          Input array of arbitrary shape.
      pad_amt : int
          Amount of padding to prepend.
      num : int
          Depth into `arr` along `axis` to calculate minimum.
          Range: [1, `arr.shape[axis]`] or None (entire axis)
      axis : int
          Axis along which to pad `arr`.
  
      Returns
      -------
      padarr : ndarray
          Output array, with `pad_amt` values prepended along `axis`. The
          prepended region is the minimum of the first `num` values along
          `axis`.
docstring_summary: Prepend `pad_amt` minimum values along `axis`.
docstring_tokens: Prepend pad_amt minimum values along axis .
function: def _prepend_min(arr, pad_amt, num, axis=-1):
      """
      Prepend `pad_amt` minimum values along `axis`.
  
      Parameters
      ----------
      arr : ndarray
          Input array of arbitrary shape.
      pad_amt : int
          Amount of padding to prepend.
      num : int
          Depth into `arr` along `axis` to calculate minimum.
          Range: [1, `arr.shape[axis]`] or None (entire axis)
      axis : int
          Axis along which to pad `arr`.
  
      Returns
      -------
      padarr : ndarray
          Output array, with `pad_amt` values prepended along `axis`. The
          prepended region is the minimum of the first `num` values along
          `axis`.
  
      """
      if pad_amt == 0:
          return arr
  
      # Equivalent to edge padding for single value, so do that instead
      if num == 1:
          return _prepend_edge(arr, pad_amt, axis)
  
      # Use entire array if `num` is too large
      if num is not None:
          if num >= arr.shape[axis]:
              num = None
  
      # Slice a chunk from the edge to calculate stats on
      min_slice = _slice_first(arr.shape, num, axis=axis)
  
      # Extract slice, calculate min
      min_chunk = arr[min_slice].min(axis=axis, keepdims=True)
  
      # Concatenate `arr` with `min_chunk`, extended along `axis` by `pad_amt`
      return _do_prepend(arr, min_chunk.repeat(pad_amt, axis), axis=axis)
function_tokens: def _prepend_min ( arr , pad_amt , num , axis = - 1 ) : if pad_amt == 0 : return arr # Equivalent to edge padding for single value, so do that instead if num == 1 : return _prepend_edge ( arr , pad_amt , axis ) # Use entire array if `num` is too large if num is not None : if num >= arr . shape [ axis ] : num = None # Slice a chunk from the edge to calculate stats on min_slice = _slice_first ( arr . shape , num , axis = axis ) # Extract slice, calculate min min_chunk = arr [ min_slice ] . min ( axis = axis , keepdims = True ) # Concatenate `arr` with `min_chunk`, extended along `axis` by `pad_amt` return _do_prepend ( arr , min_chunk . repeat ( pad_amt , axis ) , axis = axis )