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NEW QUESTION: 1
プロジェクトをステージに分割する利点は何ですか？
A. プロジェクトの概要は、各段階の終わりに向かって更新できます。
B. プロジェクトは、プロジェクトマネージャーがステージごとに承認できます。
C. プロジェクト委員会は、プロジェクトの管理をチームマネージャーに委任できます。
D. 詳細な計画は、イベントが開催される時間に近づいて準備できます。
Answer: D

NEW QUESTION: 2
原価を無効にしながら、時間関連データの活動配分を有効化します。
割り当て。以下のインフォタイプのどれを設定しますか？
正しい答えを選んでください。
応答：
A. IT2011（タイムイベント）
B. IT2005（残業）
C. IT2004（出荷予定）
D. IT2001（欠席）
Answer: D

NEW QUESTION: 3
You need to use the Python language to build a sampling strategy for the global penalty detection models.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: import pytorch as deeplearninglib
Box 2: ..DistributedSampler(Sampler)..
DistributedSampler(Sampler):
Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with class:`torch.nn.parallel.DistributedDataParallel`. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it.
Scenario: Sampling must guarantee mutual and collective exclusively between local and global segmentation models that share the same features.
Box 3: optimizer = deeplearninglib.train. GradientDescentOptimizer(learning_rate=0.10) Incorrect Answers: ..SGD..
Scenario: All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too slow.
Box 4: .. nn.parallel.DistributedDataParallel..
DistributedSampler(Sampler): The sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`.
References:
https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py