docstr: Lazily load an Xarray representing 1 month of data.
  
  The Xarray's data variable is a dask.array that's lazily constructed.
  Therefore, creating the Xarray object doesn't consume any memory.
  But computing the Xarray will.
docstr_tok: ["Lazily","load","an","Xarray","representing","1","month","of","data",".","The","Xarray","'s","data","variable","is","a","dask.array","that","'s","lazily","constructed",".","Therefore",",","creating","the","Xarray","object","does","n't","consume","any","memory",".","But","computing","the","Xarray","will","."]
code: def lazy_load_xarray_one_month(test_spec: TestSpec) -> xarray.Dataset:
      """
      Lazily load an Xarray representing 1 month of data.
  
      The Xarray's data variable is a dask.array that's lazily constructed.
      Therefore, creating the Xarray object doesn't consume any memory.
      But computing the Xarray will.
      """
      dask_array_lists = list()
  
      array_dtype = np.float32
      # Create chunks with power-of-two sizes so that downstream
      # FFT computations will work correctly.
      rechunk_size = 2 << 23
  
      # Create MINUTES_IN_A_MONTH number of Delayed objects where
      # each Delayed object is loading an array.
      for i in range(0, MINUTES_IN_A_MONTH):
          dask_arr = dask.array.from_delayed(
              dask.delayed(LoadRoutines.load_array_one_minute)(
                  test_spec
              ),
              shape=INPUT_SHAPE,
              dtype=array_dtype,
          )
          dask_array_lists.append(dask_arr)
  
      # Return the final dask.array in an Xarray
      return xarray.Dataset(
          data_vars={
              "data_var": (
                  ["channel", "time"],
                  dask.array.rechunk(
                      dask.array.concatenate(dask_array_lists, axis=1),
                      chunks=(INPUT_SHAPE[0], rechunk_size),
                  ),
              )
          },
          coords={"channel": ("channel", np.arange(INPUT_SHAPE[0]))},
          attrs={"hello": "world"},
      )
  
imports: import argparse
  import json
  import logging
  import math
  import os
  import random
  import time
  from copy import copy, deepcopy
  from typing import List, Tuple
  import dask.array
  import numpy
  import xarray
  import ray
  from ray._private.test_utils import monitor_memory_usage
  from ray.util.dask import ray_dask_get
label_desc: Use image node_id 1 for calling the LoadRoutines obj's underlying member method code with example usage: obj.lazy_load_xarray_one_month(test_spec) and returns: xarray