from functools import partial

# Mock dataset in a dictionary form, similar to what you might find in a data processing library
dataset = {
    "train": [
        {"text": "Hello world", "id": 1},
        {"text": "Partial functions are cool", "id": 2},
    ]
}

# Function to preprocess the dataset
def prepare_train_dataset(example):
    # Let's say we just transform the text to uppercase for simplicity
    return {"text": example["text"].upper()}

# Columns to remove from the dataset after the transformation
columns_to_remove = ['id']

# Creating a mock map function for the dataset
def dataset_map(batch, function, remove_columns, batched, batch_size):
    # Process each batch
    transformed_data = [function(example) for example in batch]
    # Remove specified columns
    for item in transformed_data:
        for column in remove_columns:
            item.pop(column, None)
    return transformed_data

# Using partial to pre-configure the map function
map_fn_train = partial(
    dataset_map,
    batch=dataset["train"],
    function=prepare_train_dataset,
    remove_columns=columns_to_remove,
    batched=True,
    batch_size=2  # Assuming we process all data in one batch for simplicity
)

# Using the configured function
transformed_dataset = map_fn_train()
print(transformed_dataset)