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# in a courtesy of Caffe's filter visualization example
# http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb

def imshow_grid(data, height=None, width=None, normalize=False, padsize=1, padval=0):
    '''
    Take an array of shape (N, H, W) or (N, H, W, C)
    and visualize each (H, W) image in a grid style (height x width).
    '''
    if normalize:
        data -= data.min()

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-- Make sure TS syntax tree is updated when needed by plugin (with some throttling)
-- even if the `highlight` module is not enabled.
-- See https://github.com/nvim-treesitter/nvim-treesitter/issues/2492
_G.TreesitterParse = function()
  local lang = ts_parsers.ft_to_lang(vim.bo.filetype)
  local parser = ts_parsers.get_parser(vim.fn.bufnr(), lang)
  if parser then
    parser:parse()
    return true
  else

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Displaying Configurations in TensorBoard

This is a simple demonstration of displaying training configuration in a TensorBoard, using text summaries.

Screenshot

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local ts_utils = require("nvim-treesitter.ts_utils")

local M = {}

-- Credit: https://www.reddit.com/r/neovim/comments/tge2ty/python_toggle_fstring_using_treesitter/
M.toggle_fstring = function()
  local winnr = 0
  local cursor = vim.api.nvim_win_get_cursor(winnr)
  local node = ts_utils.get_node_at_cursor()  ---@type TSNode?