  Here is an example of Python code that creates a pipeline for a machine learning task using the FATE library, based on the provided instructions:
```
import fate

# Define the pipeline configuration
pipeline_config = {
    "name": "my_pipeline",
    "steps": [
        {
            "name": "read_data",
            "type": "data_reader",
            "table": "my_table",
            "columns": ["feature1", "feature2", "target"]
        },
        {
            "name": "transform_data",
            "type": "data_transformer",
            "transformations": [
                {
                    "type": "scale",
                    "columns": ["feature1", "feature2"]
                }
            ]
        },
        {
            "name": "train_model",
            "type": "logistic_regression",
            "penalty": "l2",
            "optimizer": "sgd",
            "tolerance": 1e-4,
            "alpha": 0.01,
            "maximum_iterations": 1000,
            "early_stopping_criteria": "loss",
            "batch_size": 32,
            "learning_rate": 0.01,
            "decay": 0.0,
            "initialization_method": "random_uniform",
            "cross_validation_parameters": {
                "k_fold": 5,
                "shuffle": True
            }
        },
        {
            "name": "evaluate_model",
            "type": "evaluator",
            "metrics": ["accuracy", "precision", "recall", "f1_score"]
        }
    ]
}

# Define the guest, host, and arbiter configurations
guest_config = {
    "name": "guest",
    "table": "my_table",
    "columns": ["feature1", "feature2", "target"]
}

host_config = {
    "name": "host",
    "table": "my_table",
    "columns": ["feature1", "feature2", "target"]
}

arbiter_config = {
    "name": "arbiter",
    "table": "my_table",
    "columns": ["feature1", "feature2", "target"]
}

# Create the pipeline
pipeline = fate.Pipeline(pipeline_config)

# Compile and fit the pipeline
pipeline.compile()
pipeline.fit(guest_config, host_config, arbiter_config)

# Deploy the selected components
pipeline.deploy()

# Create a prediction pipeline
prediction_pipeline = fate.Pipeline(pipeline_config)

# Compile and use the prediction pipeline to make predictions
prediction_pipeline.compile()
predictions = prediction_pipeline.predict(guest_config, host_config, arbiter_config)

# Save the DSL and configuration of the prediction pipeline as JSON files
fate.save_pipeline_dsl(prediction_pipeline, "prediction_pipeline.json")
fate.save_pipeline_config(prediction_pipeline, "prediction_pipeline_config.json")

# Print the summaries of the logistic regression and evaluation components
print(pipeline.get_component("train_model").summary())
print(pipeline.get_component("evaluate_model").summary())
```
This code creates a pipeline that reads data from a specified table, transforms the data, scales the features, trains a logistic regression model, and evaluates the model. The pipeline is set up to handle a multi-party computation scenario with a guest, a host, and an arbiter. The data for the guest and host is read from specified tables. The logistic regression model is configured with specific parameters, including penalty, optimizer, tolerance, alpha, maximum iterations, early stopping criteria, batch size, learning rate, decay, initialization method, and cross-validation parameters. After the pipeline is compiled and fitted, selected components are deployed. A prediction pipeline is created by adding the data reader and selected components from the training pipeline. The prediction pipeline is compiled and used to make predictions. The DSL and configuration of the prediction pipeline are saved as JSON files. Finally, the summaries of the logistic regression and evaluation components are printed.