How to use the autofit.util.create_path function in autofit

def load_test_interferometer(data_name, instrument):

    dataset_path = af.util.create_path(
        path=test_path, folders=["dataset", "interferometer", data_name, instrument]
    )

    return al.Interferometer.from_fits(
        visibilities_path=f"{dataset_path}/visibilities.fits",
        noise_map_path=f"{dataset_path}/noise_map.fits",
        uv_wavelengths_path=f"{dataset_path}/uv_wavelengths.fits",
    )

"""
The 'dataset_type' describes the type of data being simulated (in this case, _Imaging_ data) and 'dataset_name' 
gives it a descriptive name. They define the folder the dataset is output to on your hard-disk:

 - The image will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/image.fits'.
 - The noise-map will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/lens_name/noise_map.fits'.
 - The psf will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/psf.fits'.
"""
dataset_type = "chapter_2"
dataset_name = "lens_sis__source_exp_x2"

"""
Create the path where the dataset will be output, which in this case is:
'/autolens_workspace/howtolens/dataset/chapter_2/lens_sis__source_exp/'
"""
dataset_path = af.util.create_path(
    path=workspace_path, folders=["howtolens", "dataset", dataset_type, dataset_name]
)

"""
The grid used to simulate the image. 

For simulating an image of a strong lens, we recommend using a GridIterate object. This represents a grid of (y,x) 
coordinates like an ordinary Grid, but when the light-profile's image is evaluated below (using the Tracer) the 
sub-size of the grid is iteratively increased (in steps of 2, 4, 8, 16, 24) until the input fractional accuracy of 
99.99% is met.

This ensures that the divergent and bright central regions of the source galaxy are fully resolved when determining the
total flux emitted within a pixel.
"""
grid = al.GridIterate.uniform(
    shape_2d=(100, 100),

"""
The 'dataset_type' describes the type of data being simulated (in this case, _Imaging_ data) and 'dataset_name' 
gives it a descriptive name. They define the folder the dataset is output to on your hard-disk:

 - The image will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/image.fits'.
 - The noise-map will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/lens_name/noise_map.fits'.
 - The psf will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/psf.fits'.
"""
dataset_type = "chapter_2"
dataset_name = "lens_sersic_sie__source_sersic"

"""
Create the path where the dataset will be output, which in this case is:
'/autolens_workspace/howtolens/dataset/chapter_2/lens_sis__source_exp/'
"""
dataset_path = af.util.create_path(
    path=workspace_path, folders=["howtolens", "dataset", dataset_type, dataset_name]
)

"""
The grid used to simulate the image. 

For simulating an image of a strong lens, we recommend using a GridIterate object. This represents a grid of (y,x) 
coordinates like an ordinary Grid, but when the light-profile's image is evaluated below (using the Tracer) the 
sub-size of the grid is iteratively increased (in steps of 2, 4, 8, 16, 24) until the input fractional accuracy of 
99.99% is met.

This ensures that the divergent and bright central regions of the source galaxy are fully resolved when determining the
total flux emitted within a pixel.
"""
grid = al.GridIterate.uniform(
    shape_2d=(100, 100),

"""
The 'dataset_type' describes the type of data being simulated (in this case, _Imaging_ data) and 'dataset_name' 
gives it a descriptive name. They define the folder the dataset is output to on your hard-disk:

 - The image will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/image.fits'.
 - The noise-map will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/lens_name/noise_map.fits'.
 - The psf will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/psf.fits'.
"""
dataset_type = "chapter_4"
dataset_name = "lens_sie__source_sersic"

"""
Create the path where the dataset will be output, which in this case is:
'/autolens_workspace/howtolens/dataset/chapter_2/lens_sis__source_exp/'
"""
dataset_path = af.util.create_path(
    path=workspace_path, folders=["howtolens", "dataset", dataset_type, dataset_name]
)

"""
The grid used to simulate the image. 

For simulating an image of a strong lens, we recommend using a GridIterate object. This represents a grid of (y,x) 
coordinates like an ordinary Grid, but when the light-profile's image is evaluated below (using the Tracer) the 
sub-size of the grid is iteratively increased (in steps of 2, 4, 8, 16, 24) until the input fractional accuracy of 
99.99% is met.

This ensures that the divergent and bright central regions of the source galaxy are fully resolved when determining the
total flux emitted within a pixel.
"""
grid = al.GridIterate.uniform(
    shape_2d=(100, 100),

"""
The 'dataset_type' describes the type of data being simulated (in this case, _Imaging_ data) and 'dataset_name' 
gives it a descriptive name. They define the folder the dataset is output to on your hard-disk:

 - The image will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/image.fits'.
 - The noise-map will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/lens_name/noise_map.fits'.
 - The psf will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/psf.fits'.
"""
dataset_type = "chapter_5"
dataset_name = "lens_sersic_sie__source_sersic_x4"

"""
Create the path where the dataset will be output, which in this case is:
'/autolens_workspace/howtolens/dataset/chapter_2/lens_sis__source_sersic_x5/'
"""
dataset_path = af.util.create_path(
    path=workspace_path, folders=["howtolens", "dataset", dataset_type, dataset_name]
)

"""
The grid used to simulate the image. 

For simulating an image of a strong lens, we recommend using a GridIterate object. This represents a grid of (y,x) 
coordinates like an ordinary Grid, but when the light-profile's image is evaluated below (using the Tracer) the 
sub-size of the grid is iteratively increased (in steps of 2, 4, 8, 16, 24) until the input fractional accuracy of 
99.99% is met.

This ensures that the divergent and bright central regions of the source galaxy are fully resolved when determining the
total flux emitted within a pixel.
"""
grid = al.GridIterate.uniform(
    shape_2d=(100, 100),

"""
The 'dataset_type' describes the type of data being simulated (in this case, _Imaging_ data) and 'dataset_name' 
gives it a descriptive name. They define the folder the dataset is output to on your hard-disk:

 - The image will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/image.fits'.
 - The noise-map will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/lens_name/noise_map.fits'.
 - The psf will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/psf.fits'.
"""
dataset_type = "chapter_4"
dataset_name = "lens_sie__source_sersic_x5"

"""
Create the path where the dataset will be output, which in this case is:
'/autolens_workspace/howtolens/dataset/chapter_2/lens_sis__source_sersic_x5/'
"""
dataset_path = af.util.create_path(
    path=workspace_path, folders=["howtolens", "dataset", dataset_type, dataset_name]
)

"""
The grid used to simulate the image. 

For simulating an image of a strong lens, we recommend using a GridIterate object. This represents a grid of (y,x) 
coordinates like an ordinary Grid, but when the light-profile's image is evaluated below (using the Tracer) the 
sub-size of the grid is iteratively increased (in steps of 2, 4, 8, 16, 24) until the input fractional accuracy of 
99.99% is met.

This ensures that the divergent and bright central regions of the source galaxy are fully resolved when determining the
total flux emitted within a pixel.
"""
grid = al.GridIterate.uniform(
    shape_2d=(100, 100),

"""
The 'dataset_type' describes the type of data being simulated (in this case, _Imaging_ data) and 'dataset_name' 
gives it a descriptive name. They define the folder the dataset is output to on your hard-disk:

 - The image will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/image.fits'.
 - The noise-map will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/lens_name/noise_map.fits'.
 - The psf will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/psf.fits'.
"""
dataset_type = "chapter_4"
dataset_name = "lens_sersic_sie__source_sersic"

"""
Create the path where the dataset will be output, which in this case is:
'/autolens_workspace/howtolens/dataset/chapter_2/lens_sis__source_exp/'
"""
dataset_path = af.util.create_path(
    path=workspace_path, folders=["howtolens", "dataset", dataset_type, dataset_name]
)

"""
The grid used to simulate the image. 

For simulating an image of a strong lens, we recommend using a GridIterate object. This represents a grid of (y,x) 
coordinates like an ordinary Grid, but when the light-profile's image is evaluated below (using the Tracer) the 
sub-size of the grid is iteratively increased (in steps of 2, 4, 8, 16, 24) until the input fractional accuracy of 
99.99% is met.

This ensures that the divergent and bright central regions of the source galaxy are fully resolved when determining the
total flux emitted within a pixel.
"""
grid = al.GridIterate.uniform(
    shape_2d=(150, 150),

"""
The 'dataset_type' describes the type of data being simulated (in this case, _Imaging_ data) and 'dataset_name' 
gives it a descriptive name. They define the folder the dataset is output to on your hard-disk:

 - The image will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/image.fits'.
 - The noise-map will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/lens_name/noise_map.fits'.
 - The psf will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/psf.fits'.
"""
dataset_type = "chapter_2"
dataset_name = "lens_sis__source_exp"

"""
Create the path where the dataset will be output, which in this case is:
'/autolens_workspace/howtolens/dataset/chapter_2/lens_sis__source_exp/'
"""
dataset_path = af.util.create_path(
    path=workspace_path, folders=["howtolens", "dataset", dataset_type, dataset_name]
)

"""
The grid used to simulate the image. 

For simulating an image of a strong lens, we recommend using a GridIterate object. This represents a grid of (y,x) 
coordinates like an ordinary Grid, but when the light-profile's image is evaluated below (using the Tracer) the 
sub-size of the grid is iteratively increased (in steps of 2, 4, 8, 16, 24) until the input fractional accuracy of 
99.99% is met.

This ensures that the divergent and bright central regions of the source galaxy are fully resolved when determining the
total flux emitted within a pixel.
"""
grid = al.GridIterate.uniform(
    shape_2d=(100, 100),

"""
The 'dataset_type' describes the type of data being simulated (in this case, _Imaging_ data) and 'dataset_name' 
gives it a descriptive name. They define the folder the dataset is output to on your hard-disk:

 - The image will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/image.fits'.
 - The noise-map will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/lens_name/noise_map.fits'.
 - The psf will be output to '/autolens_workspace/dataset/dataset_type/dataset_name/psf.fits'.
"""
dataset_type = "chapter_5"
dataset_name = "lens_sie__source_sersic"

"""
Create the path where the dataset will be output, which in this case is:
'/autolens_workspace/howtolens/dataset/chapter_2/lens_sis__source_exp/'
"""
dataset_path = af.util.create_path(
    path=workspace_path, folders=["howtolens", "dataset", dataset_type, dataset_name]
)

"""
The grid used to simulate the image. 

For simulating an image of a strong lens, we recommend using a GridIterate object. This represents a grid of (y,x) 
coordinates like an ordinary Grid, but when the light-profile's image is evaluated below (using the Tracer) the 
sub-size of the grid is iteratively increased (in steps of 2, 4, 8, 16, 24) until the input fractional accuracy of 
99.99% is met.

This ensures that the divergent and bright central regions of the source galaxy are fully resolved when determining the
total flux emitted within a pixel.
"""
grid = al.GridIterate.uniform(
    shape_2d=(150, 150),