custom_tensorflow_keras_nlp/src/main.py [24:45]:
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def load_embeddings(base_dir):
    embedding_matrix = np.load(os.path.join(base_dir, "docs-embedding-matrix.npy"))
    return embedding_matrix

def parse_args():
    """Acquire hyperparameters and directory locations passed by SageMaker"""
    parser = argparse.ArgumentParser()

    # Hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument("--epochs", type=int, default=1)
    parser.add_argument("--learning_rate", type=float, default=0.001)
    parser.add_argument("--num_classes", type=int, default=4)
    parser.add_argument("--max_seq_len", type=int, default=40)

    # Data, model, and output directories
    parser.add_argument("--output-data-dir", type=str, default=os.environ.get("SM_OUTPUT_DATA_DIR"))
    parser.add_argument("--model-dir", type=str, default=os.environ.get("SM_MODEL_DIR"))
    parser.add_argument("--train", type=str, default=os.environ.get("SM_CHANNEL_TRAIN"))
    parser.add_argument("--test", type=str, default=os.environ.get("SM_CHANNEL_TEST"))
    parser.add_argument("--embeddings", type=str, default=os.environ.get("SM_CHANNEL_EMBEDDINGS"))

    return parser.parse_known_args()
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pytorch_alternatives/custom_pytorch_nlp/src/main.py [70:91]:
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def load_embeddings(base_dir):
    embedding_matrix = np.load(os.path.join(base_dir, "docs-embedding-matrix.npy"))
    return embedding_matrix

def parse_args():
    """Acquire hyperparameters and directory locations passed by SageMaker"""
    parser = argparse.ArgumentParser()

    # Hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument("--epochs", type=int, default=1)
    parser.add_argument("--learning_rate", type=float, default=0.001)
    parser.add_argument("--num_classes", type=int, default=4)
    parser.add_argument("--max_seq_len", type=int, default=40)

    # Data, model, and output directories
    parser.add_argument("--output-data-dir", type=str, default=os.environ.get("SM_OUTPUT_DATA_DIR"))
    parser.add_argument("--model-dir", type=str, default=os.environ.get("SM_MODEL_DIR"))
    parser.add_argument("--train", type=str, default=os.environ.get("SM_CHANNEL_TRAIN"))
    parser.add_argument("--test", type=str, default=os.environ.get("SM_CHANNEL_TEST"))
    parser.add_argument("--embeddings", type=str, default=os.environ.get("SM_CHANNEL_EMBEDDINGS"))

    return parser.parse_known_args()
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