code: def tri(N, M=None, k=0, dtype=float, chunks="auto", *, like=None):
      if M is None:
          M = N
  
      chunks = normalize_chunks(chunks, shape=(N, M), dtype=dtype)
  
      m = greater_equal(
          arange(N, chunks=chunks[0][0], like=like).reshape(1, N).T,
          arange(-k, M - k, chunks=chunks[1][0], like=like),
      )
  
      # Avoid making a copy if the requested type is already bool
      m = m.astype(dtype, copy=False)
  
      return m
  
imports: from __future__ import annotations
  import itertools
  from collections.abc import Sequence
  from functools import partial
  from itertools import product
  from numbers import Integral, Number
  import numpy
  from tlz import sliding_window
  from dask.array import chunk
  from dask.array.backends import array_creation_dispatch
  from dask.array.core import Array, asarray, block, blockwise, broadcast_arrays, broadcast_to, concatenate, normalize_chunks, stack
  from dask.array.numpy_compat import AxisError
  from dask.array.ufunc import greater_equal, rint
  from dask.array.utils import meta_from_array
  from dask.array.wrap import empty, full, ones, zeros
  from dask.base import tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import cached_cumsum, derived_from, is_cupy_type
label_desc: Use image node_id 14 for calling a global function with example usage: tri(N, M, k, dtype, chunks) and returns: m