fix cuspatial.haversine_distance() example in multi-tenant notebook

[jameslamb]

Fixes the cuspatial.haversine_distance() example using the public NYC taxi data hosted on GSC (GSC console link).

Running through the notebook code for that example (the same one that ends up at https://docs.rapids.ai/deployment/nightly/examples/rapids-autoscaling-multi-tenant-kubernetes/notebook/), I encountered 3 issues:

1. needed to authenticate with GCP

ValueError: An error occurred while calling the read_parquet method registered to the cudf backend.
Original Message: An error occurred while calling the read_parquet method registered to the pandas backend.
Original Message: Invalid gcloud credentials

2. that GCS bucket contains some files at path gcs://anaconda-public-data/nyc-taxi/2015.parquet that are not actually parquet files

3. cuspatial.haversine_distance() expects to receive 2 cuspatial.GeoSeries objects

TypeError('haversine_distance() takes 2 positional arguments but 4 were given')

Looks like that changed here: rapidsai/cuspatial#924.

This resolves those issues.

How I tested this

Following the instructions from https://docs.rapids.ai/install#install-rapids, ran jupyter lab in a RAPIDS container like this:

docker run \
    --gpus all \
    --pull always \
    --rm \
    -it \
    --shm-size=1g \
    --ulimit memlock=-1 \
    --ulimit stack=67108864 \
    -p 8888:8888 \
    -p 8787:8787 \
    -p 8786:8786 \
    rapidsai/notebooks:24.02a-cuda12.0-py3.10

Then ran this notebook code (just the LocalCUDACluster parts and below), on a machine with a few 80GB H100s. Confirmed that data was pulled successfully without needing to authenticate with GCP, and that cuspatial.haversine_distance() ran without error and produced plausible-looking results.

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[jameslamb]

Since that bucket is public, authenticating with GCP isn't necessary. For reference: https://gcsfs.readthedocs.io/en/stable/#credentials

[jacobtomlinson]

This looks great thanks @jameslamb