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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 AutoML Text entity extractionn model
Installation
Install the latest version of AutoML SDK.
Step1:  Install the Google cloud-storage library as well.
Step2:  Restart the Kernel
Once you've installed the AutoML SDK and Google cloud-storage, you need to restart the notebook kernel so it can find the packages.
Step3:  Before you begin
GPU run-time
Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select Runtime > Change Runtime Type > GPU
Set up your GCP project
The following steps are required, regardless of your notebook environment.
Select or create a GCP project. When you first create an account, you get a $300 free credit towards your compute/storage costs.
Make sure that billing is enabled for your project.
Enable the AutoML APIs and Compute Engine APIs.
Google Cloud SDK is already installed in AutoML Notebooks.
Enter your project ID in the cell below. Then run the  cell to make sure the
Cloud SDK uses the right project for all the commands in this notebook.
Note
Step4:  Region
You can also change the REGION variable, which is used for operations
throughout the rest of this notebook.  Below are regions supported for AutoML. We recommend when possible, to choose the region closest to you.
Americas
Step5:  Timestamp
If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append onto the name of resources which will be created in this tutorial.
Step6:  Authenticate your GCP account
If you are using AutoML Notebooks, your environment is already
authenticated. Skip this step.
Note
Step7:  Create a Cloud Storage bucket
The following steps are required, regardless of your notebook environment.
This tutorial is designed to use training data that is in a public Cloud Storage bucket and a local Cloud Storage bucket for your batch predictions. You may alternatively use your own training data that you have stored in a local Cloud Storage bucket.
Set the name of your Cloud Storage bucket below. It must be unique across all Cloud Storage buckets.
Step8:  Only if your bucket doesn't already exist
Step9:  Finally, validate access to your Cloud Storage bucket by examining its contents
Step10:  Set up variables
Next, set up some variables used throughout the tutorial.
Import libraries and define constants
Import AutoML SDK
Import the AutoM SDK into our Python environment.
Step11:  AutoML constants
Setup up the following constants for AutoML
Step12:  Clients
The AutoML SDK works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the server (AutoML).
You will use several clients in this tutorial, so set them all up upfront.
Step13:  Example output
Step14:  Example output
Step15:  Response
Step16:  Example output
Step17:  projects.locations.datasets.importData
Request
Step18:  Example output
Step19:  Response
Step20:  Example output
Step21:  Example output
Step22:  Response
Step23:  Example output
Step24:  Evaluate the model
projects.locations.models.modelEvaluations.list
Call
Step25:  Response
Step26:  Example output
Step27:  Response
Step28:  Example output
Step29:  Example output
Step30:  Example output
Step31:  Response
Step32:  Example output
Step33:  Example output
Step34:  Response
Step35:  Example output
Step36:  Request
Step37:  Example output
Step38:  Response
Step39:  Example output
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Python Code:
! pip3 install google-cloud-automl
Explanation: AutoML Text entity extractionn model
Installation
Install the latest version of AutoML SDK.
End of explanation
! pip3 install google-cloud-storage
Explanation: Install the Google cloud-storage library as well.
End of explanation
import os
if not os.getenv("AUTORUN"):
    # Automatically restart kernel after installs
    import IPython
    app = IPython.Application.instance()
    app.kernel.do_shutdown(True)
Explanation: Restart the Kernel
Once you've installed the AutoML SDK and Google cloud-storage, you need to restart the notebook kernel so it can find the packages.
End of explanation
PROJECT_ID = "[your-project-id]"  # @param {type:"string"}
if PROJECT_ID == "" or PROJECT_ID is None or PROJECT_ID == "[your-project-id]":
    # Get your GCP project id from gcloud
    shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null
    PROJECT_ID = shell_output[0]
    print("Project ID:", PROJECT_ID)
! gcloud config set project $PROJECT_ID
Explanation: Before you begin
GPU run-time
Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select Runtime > Change Runtime Type > GPU
Set up your GCP project
The following steps are required, regardless of your notebook environment.
Select or create a GCP project. When you first create an account, you get a $300 free credit towards your compute/storage costs.
Make sure that billing is enabled for your project.
Enable the AutoML APIs and Compute Engine APIs.
Google Cloud SDK is already installed in AutoML Notebooks.
Enter your project ID in the cell below. Then run the  cell to make sure the
Cloud SDK uses the right project for all the commands in this notebook.
Note: Jupyter runs lines prefixed with ! as shell commands, and it interpolates Python variables prefixed with $ into these commands.
End of explanation
REGION = "us-central1"  # @param {type: "string"}
Explanation: Region
You can also change the REGION variable, which is used for operations
throughout the rest of this notebook.  Below are regions supported for AutoML. We recommend when possible, to choose the region closest to you.
Americas: us-central1
Europe: europe-west4
Asia Pacific: asia-east1
You cannot use a Multi-Regional Storage bucket for training with AutoML. Not all regions provide support for all AutoML services. For the latest support per region, see Region support for AutoML services
End of explanation
from datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")
Explanation: Timestamp
If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append onto the name of resources which will be created in this tutorial.
End of explanation
import os
import sys
# If you are running this notebook in Colab, run this cell and follow the
# instructions to authenticate your Google Cloud account. This provides access
# to your Cloud Storage bucket and lets you submit training jobs and prediction
# requests.
# If on Vertex, then don't execute this code
if not os.path.exists("/opt/deeplearning/metadata/env_version"):
    if "google.colab" in sys.modules:
        from google.colab import auth as google_auth
        google_auth.authenticate_user()
    # If you are running this tutorial in a notebook locally, replace the string
    # below with the path to your service account key and run this cell to
    # authenticate your Google Cloud account.
    else:
        %env GOOGLE_APPLICATION_CREDENTIALS your_path_to_credentials.json
    # Log in to your account on Google Cloud
    ! gcloud auth login
Explanation: Authenticate your GCP account
If you are using AutoML Notebooks, your environment is already
authenticated. Skip this step.
Note: If you are on an AutoML notebook and run the cell, the cell knows to skip executing the authentication steps.
End of explanation
BUCKET_NAME = "[your-bucket-name]"  # @param {type:"string"}
if BUCKET_NAME == "" or BUCKET_NAME is None or BUCKET_NAME == "[your-bucket-name]":
    BUCKET_NAME = PROJECT_ID + "aip-" + TIMESTAMP
Explanation: Create a Cloud Storage bucket
The following steps are required, regardless of your notebook environment.
This tutorial is designed to use training data that is in a public Cloud Storage bucket and a local Cloud Storage bucket for your batch predictions. You may alternatively use your own training data that you have stored in a local Cloud Storage bucket.
Set the name of your Cloud Storage bucket below. It must be unique across all Cloud Storage buckets.
End of explanation
! gsutil mb -l $REGION gs://$BUCKET_NAME
Explanation: Only if your bucket doesn't already exist: Run the following cell to create your Cloud Storage bucket.
End of explanation
! gsutil ls -al gs://$BUCKET_NAME
Explanation: Finally, validate access to your Cloud Storage bucket by examining its contents:
End of explanation
import json
import os
import sys
import time
from google.cloud import automl
from google.protobuf.json_format import MessageToJson
from google.protobuf.struct_pb2 import Value
Explanation: Set up variables
Next, set up some variables used throughout the tutorial.
Import libraries and define constants
Import AutoML SDK
Import the AutoM SDK into our Python environment.
End of explanation
# AutoM location root path for your dataset, model and endpoint resources
PARENT = "projects/" + PROJECT_ID + "/locations/" + REGION
Explanation: AutoML constants
Setup up the following constants for AutoML:
PARENT: The AutoM location root path for dataset, model and endpoint resources.
End of explanation
def automl_client():
    return automl.AutoMlClient()
def prediction_client():
    return automl.PredictionServiceClient()
def operations_client():
    return automl.AutoMlClient()._transport.operations_client
clients = {}
clients["automl"] = automl_client()
clients["prediction"] = prediction_client()
clients["operations"] = operations_client()
for client in clients.items():
    print(client)
IMPORT_FILE = "gs://cloud-ml-data/NL-entity/dataset.csv"
! gsutil cat $IMPORT_FILE | head -n 10
Explanation: Clients
The AutoML SDK works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the server (AutoML).
You will use several clients in this tutorial, so set them all up upfront.
End of explanation
dataset = {
    "display_name": "entity_" + TIMESTAMP,
    "text_extraction_dataset_metadata": {},
}
print(
    MessageToJson(
        automl.CreateDatasetRequest(parent=PARENT, dataset=dataset).__dict__["_pb"]
    )
)
Explanation: Example output:
TRAIN,gs://cloud-ml-data/NL-entity/train.jsonl
TEST,gs://cloud-ml-data/NL-entity/test.jsonl
VALIDATION,gs://cloud-ml-data/NL-entity/validation.jsonl
Create a dataset
Prepare data
projects.locations.datasets.create
Request
End of explanation
request = clients["automl"].create_dataset(parent=PARENT, dataset=dataset)
Explanation: Example output:
{
  "parent": "projects/migration-ucaip-training/locations/us-central1",
  "dataset": {
    "displayName": "entity_20210303201139",
    "textExtractionDatasetMetadata": {}
  }
}
Call
End of explanation
result = request.result()
print(MessageToJson(result.__dict__["_pb"]))
Explanation: Response
End of explanation
# The full unique ID for the dataset
dataset_id = result.name
# The short numeric ID for the dataset
dataset_short_id = dataset_id.split("/")[-1]
print(dataset_id)
Explanation: Example output:
{
  "name": "projects/116273516712/locations/us-central1/datasets/TEN4244124229064196096"
}
End of explanation
input_config = {"gcs_source": {"input_uris": [IMPORT_FILE]}}
print(
    MessageToJson(
        automl.ImportDataRequest(name=dataset_id, input_config=input_config).__dict__[
            "_pb"
        ]
    )
)
Explanation: projects.locations.datasets.importData
Request
End of explanation
request = clients["automl"].import_data(name=dataset_id, input_config=input_config)
Explanation: Example output:
{
  "name": "projects/116273516712/locations/us-central1/datasets/TEN4244124229064196096",
  "inputConfig": {
    "gcsSource": {
      "inputUris": [
        "gs://cloud-ml-data/NL-entity/dataset.csv"
      ]
    }
  }
}
Call
End of explanation
result = request.result()
print(MessageToJson(result))
Explanation: Response
End of explanation
model = {
    "display_name": "entity_" + TIMESTAMP,
    "dataset_id": dataset_short_id,
    "text_extraction_model_metadata": {},
}
print(
    MessageToJson(automl.CreateModelRequest(parent=PARENT, model=model).__dict__["_pb"])
)
Explanation: Example output:
{}
Train a model
projects.locations.models.create
Request
End of explanation
request = clients["automl"].create_model(parent=PARENT, model=model)
Explanation: Example output:
{
  "parent": "projects/migration-ucaip-training/locations/us-central1",
  "model": {
    "displayName": "entity_20210303201139",
    "datasetId": "TEN4244124229064196096",
    "textExtractionModelMetadata": {}
  }
}
Call
End of explanation
result = request.result()
print(MessageToJson(result.__dict__["_pb"]))
Explanation: Response
End of explanation
# The full unique ID for the training pipeline
model_id = result.name
# The short numeric ID for the training pipeline
model_short_id = model_id.split("/")[-1]
print(model_short_id)
Explanation: Example output:
{
  "name": "projects/116273516712/locations/us-central1/models/TEN7821373765161320448"
}
End of explanation
request = clients["automl"].list_model_evaluations(parent=model_id, filter="")
Explanation: Evaluate the model
projects.locations.models.modelEvaluations.list
Call
End of explanation
import json
model_evaluations = [json.loads(MessageToJson(me.__dict__["_pb"])) for me in request]
# The evaluation slice
evaluation_slice = request.model_evaluation[0].name
print(json.dumps(model_evaluations, indent=2))
Explanation: Response
End of explanation
request = clients["automl"].get_model_evaluation(name=evaluation_slice)
Explanation: Example output:
```
[
  {
    "name": "projects/116273516712/locations/us-central1/models/TEN7821373765161320448/modelEvaluations/132746642406774043",
    "createTime": "2021-03-03T22:30:27.832506Z",
    "evaluatedExampleCount": 60,
    "textExtractionEvaluationMetrics": {
      "confidenceMetricsEntries": [
        {
          "confidenceThreshold": 0.04,
          "recall": 0.79928315,
          "precision": 0.7950089,
          "f1Score": 0.7971403
        },
        {
          "confidenceThreshold": 0.96,
          "recall": 0.75089604,
          "precision": 0.8603696,
          "f1Score": 0.80191386
        },
    # REMOVED FOR BREVITY
    {
      "confidenceThreshold": 0.43,
      "recall": 0.5913978,
      "precision": 0.57894737,
      "f1Score": 0.5851064
    },
    {
      "confidenceThreshold": 0.44,
      "recall": 0.5913978,
      "precision": 0.57894737,
      "f1Score": 0.5851064
    }
  ]
},
"displayName": "DiseaseClass"
}
]
```
projects.locations.models.modelEvaluations.get
Call
End of explanation
print(MessageToJson(request.__dict__["_pb"]))
Explanation: Response
End of explanation
import tensorflow as tf
test_item = 'Molecular basis of hexosaminidase A deficiency and pseudodeficiency in the Berks County Pennsylvania Dutch.\\tFollowing the birth of two infants with Tay-Sachs disease ( TSD ) , a non-Jewish , Pennsylvania Dutch kindred was screened for TSD carriers using the biochemical assay . A high frequency of individuals who appeared to be TSD heterozygotes was detected ( Kelly et al . , 1975 ) . Clinical and biochemical evidence suggested that the increased carrier frequency was due to at least two altered alleles for the hexosaminidase A alpha-subunit . We now report two mutant alleles in this Pennsylvania Dutch kindred , and one polymorphism . One allele , reported originally in a French TSD patient ( Akli et al . , 1991 ) , is a GT-- > AT transition at the donor splice-site of intron 9 . The second , a C-- > T transition at nucleotide 739 ( Arg247Trp ) , has been shown by Triggs-Raine et al . ( 1992 ) to be a clinically benign " pseudodeficient " allele associated with reduced enzyme activity against artificial substrate . Finally , a polymorphism [ G-- > A ( 759 ) ] , which leaves valine at codon 253 unchanged , is described'
gcs_input_uri = "gs://" + BUCKET_NAME + "/test.jsonl"
with tf.io.gfile.GFile(gcs_input_uri, "w") as f:
    data = {"id": 0, "text_snippet": {"content": test_item}}
    f.write(json.dumps(data) + "\n")
! gsutil cat $gcs_input_uri
Explanation: Example output:
```
{
  "name": "projects/116273516712/locations/us-central1/models/TEN7821373765161320448/modelEvaluations/132746642406774043",
  "createTime": "2021-03-03T22:30:27.832506Z",
  "evaluatedExampleCount": 60,
  "textExtractionEvaluationMetrics": {
    "confidenceMetricsEntries": [
      {
        "confidenceThreshold": 0.04,
        "recall": 0.79928315,
        "precision": 0.7950089,
        "f1Score": 0.7971403
      },
      {
        "confidenceThreshold": 0.96,
        "recall": 0.75089604,
        "precision": 0.8603696,
        "f1Score": 0.80191386
      },
  # REMOVED FOR BREVITY
  {
    "confidenceThreshold": 0.43,
    "recall": 0.7921147,
    "precision": 0.7935368,
    "f1Score": 0.7928251
  },
  {
    "confidenceThreshold": 0.44,
    "recall": 0.7921147,
    "precision": 0.7935368,
    "f1Score": 0.7928251
  }
]
}
}
```
Make batch predictions
Prepare files for batch prediction
End of explanation
input_config = {"gcs_source": {"input_uris": [gcs_input_uri]}}
output_config = {
    "gcs_destination": {"output_uri_prefix": "gs://" + f"{BUCKET_NAME}/batch_output/"}
}
print(
    MessageToJson(
        automl.BatchPredictRequest(
            name=model_id, input_config=input_config, output_config=output_config
        ).__dict__["_pb"]
    )
)
Explanation: Example output:
{"id": 0, "text_snippet": {"content": "Molecular basis of hexosaminidase A deficiency and pseudodeficiency in the Berks County Pennsylvania Dutch.\\tFollowing the birth of two infants with Tay-Sachs disease ( TSD ) , a non-Jewish , Pennsylvania Dutch kindred was screened for TSD carriers using the biochemical assay . A high frequency of individuals who appeared to be TSD heterozygotes was detected ( Kelly et al . , 1975 ) . Clinical and biochemical evidence suggested that the increased carrier frequency was due to at least two altered alleles for the hexosaminidase A alpha-subunit . We now report two mutant alleles in this Pennsylvania Dutch kindred , and one polymorphism . One allele , reported originally in a French TSD patient ( Akli et al . , 1991 ) , is a GT-- &gt; AT transition at the donor splice-site of intron 9 . The second , a C-- &gt; T transition at nucleotide 739 ( Arg247Trp ) , has been shown by Triggs-Raine et al . ( 1992 ) to be a clinically benign \" pseudodeficient \" allele associated with reduced enzyme activity against artificial substrate . Finally , a polymorphism [ G-- &gt; A ( 759 ) ] , which leaves valine at codon 253 unchanged , is described"}}
projects.locations.models.batchPredict
Request
End of explanation
request = clients["prediction"].batch_predict(
    name=model_id, input_config=input_config, output_config=output_config
)
Explanation: Example output:
{
  "name": "projects/116273516712/locations/us-central1/models/TEN7821373765161320448",
  "inputConfig": {
    "gcsSource": {
      "inputUris": [
        "gs://migration-ucaip-trainingaip-20210303201139/test.jsonl"
      ]
    }
  },
  "outputConfig": {
    "gcsDestination": {
      "outputUriPrefix": "gs://migration-ucaip-trainingaip-20210303201139/batch_output/"
    }
  }
}
Call
End of explanation
result = request.result()
print(MessageToJson(result.__dict__["_pb"]))
Explanation: Response
End of explanation
destination_uri = output_config["gcs_destination"]["output_uri_prefix"][:-1]
! gsutil ls $destination_uri/*
! gsutil cat $destination_uri/prediction*/*.jsonl
Explanation: Example output:
{}
End of explanation
request = clients["automl"].deploy_model(name=model_id)
Explanation: Example output:
gs://migration-ucaip-trainingaip-20210303201139/batch_output/prediction-entity_20210303201139-2021-03-03T22:30:36.292153Z/text_extraction_1.jsonl
{"textSnippet":{"content":"Molecular basis of hexosaminidase A deficiency and pseudodeficiency in the Berks County Pennsylvania Dutch.\\tFollowing the birth of two infants with Tay-Sachs disease ( TSD ) , a non-Jewish , Pennsylvania Dutch kindred was screened for TSD carriers using the biochemical assay . A high frequency of individuals who appeared to be TSD heterozygotes was detected ( Kelly et al . , 1975 ) . Clinical and biochemical evidence suggested that the increased carrier frequency was due to at least two altered alleles for the hexosaminidase A alpha-subunit . We now report two mutant alleles in this Pennsylvania Dutch kindred , and one polymorphism . One allele , reported originally in a French TSD patient ( Akli et al . , 1991 ) , is a GT-- \u003e AT transition at the donor splice-site of intron 9 . The second , a C-- \u003e T transition at nucleotide 739 ( Arg247Trp ) , has been shown by Triggs-Raine et al . ( 1992 ) to be a clinically benign \" pseudodeficient \" allele associated with reduced enzyme activity against artificial substrate . Finally , a polymorphism [ G-- \u003e A ( 759 ) ] , which leaves valine at codon 253 unchanged , is described"},"annotations":[{"displayName":"SpecificDisease","textExtraction":{"score":0.99955064,"textSegment":{"startOffset":"19","endOffset":"46","content":"hexosaminidase A deficiency"}}},{"displayName":"SpecificDisease","textExtraction":{"score":0.9995449,"textSegment":{"startOffset":"149","endOffset":"166","content":"Tay-Sachs disease"}}},{"displayName":"SpecificDisease","textExtraction":{"score":0.99939877,"textSegment":{"startOffset":"169","endOffset":"172","content":"TSD"}}},{"displayName":"Modifier","textExtraction":{"score":0.9993252,"textSegment":{"startOffset":"236","endOffset":"239","content":"TSD"}}},{"displayName":"Modifier","textExtraction":{"score":0.9993484,"textSegment":{"startOffset":"330","endOffset":"333","content":"TSD"}}},{"displayName":"Modifier","textExtraction":{"score":0.9993844,"textSegment":{"startOffset":"688","endOffset":"691","content":"TSD"}}}],"id":"0"}
Make online predictions
projects.locations.models.deploy
Call
End of explanation
result = request.result()
print(MessageToJson(result))
Explanation: Response
End of explanation
test_item = 'Molecular basis of hexosaminidase A deficiency and pseudodeficiency in the Berks County Pennsylvania Dutch.\\tFollowing the birth of two infants with Tay-Sachs disease ( TSD ) , a non-Jewish , Pennsylvania Dutch kindred was screened for TSD carriers using the biochemical assay . A high frequency of individuals who appeared to be TSD heterozygotes was detected ( Kelly et al . , 1975 ) . Clinical and biochemical evidence suggested that the increased carrier frequency was due to at least two altered alleles for the hexosaminidase A alpha-subunit . We now report two mutant alleles in this Pennsylvania Dutch kindred , and one polymorphism . One allele , reported originally in a French TSD patient ( Akli et al . , 1991 ) , is a GT-- > AT transition at the donor splice-site of intron 9 . The second , a C-- > T transition at nucleotide 739 ( Arg247Trp ) , has been shown by Triggs-Raine et al . ( 1992 ) to be a clinically benign " pseudodeficient " allele associated with reduced enzyme activity against artificial substrate . Finally , a polymorphism [ G-- > A ( 759 ) ] , which leaves valine at codon 253 unchanged , is described'
Explanation: Example output:
{}
projects.locations.models.predict
Prepare data item for online prediction
End of explanation
payload = {"text_snippet": {"content": test_item, "mime_type": "text/plain"}}
request = automl.PredictRequest(
    name=model_id,
    payload=payload,
)
print(MessageToJson(request.__dict__["_pb"]))
Explanation: Request
End of explanation
request = clients["prediction"].predict(request=request)
Explanation: Example output:
{
  "name": "projects/116273516712/locations/us-central1/models/TEN7821373765161320448",
  "payload": {
    "textSnippet": {
      "content": "Molecular basis of hexosaminidase A deficiency and pseudodeficiency in the Berks County Pennsylvania Dutch.\\tFollowing the birth of two infants with Tay-Sachs disease ( TSD ) , a non-Jewish , Pennsylvania Dutch kindred was screened for TSD carriers using the biochemical assay . A high frequency of individuals who appeared to be TSD heterozygotes was detected ( Kelly et al . , 1975 ) . Clinical and biochemical evidence suggested that the increased carrier frequency was due to at least two altered alleles for the hexosaminidase A alpha-subunit . We now report two mutant alleles in this Pennsylvania Dutch kindred , and one polymorphism . One allele , reported originally in a French TSD patient ( Akli et al . , 1991 ) , is a GT-- &gt; AT transition at the donor splice-site of intron 9 . The second , a C-- &gt; T transition at nucleotide 739 ( Arg247Trp ) , has been shown by Triggs-Raine et al . ( 1992 ) to be a clinically benign \" pseudodeficient \" allele associated with reduced enzyme activity against artificial substrate . Finally , a polymorphism [ G-- &gt; A ( 759 ) ] , which leaves valine at codon 253 unchanged , is described",
      "mimeType": "text/plain"
    }
  }
}
Call
End of explanation
print(MessageToJson(request.__dict__["_pb"]))
Explanation: Response
End of explanation
delete_dataset = True
delete_model = True
delete_bucket = True
# Delete the dataset using the AutoML fully qualified identifier for the dataset
try:
    if delete_dataset:
        clients["automl"].delete_dataset(name=dataset_id)
except Exception as e:
    print(e)
# Delete the model using the AutoML fully qualified identifier for the model
try:
    if delete_model:
        clients["automl"].delete_model(name=model_id)
except Exception as e:
    print(e)
if delete_bucket and "BUCKET_NAME" in globals():
    ! gsutil rm -r gs://$BUCKET_NAME
Explanation: Example output:
{
  "payload": [
    {
      "annotationSpecId": "8605379431835369472",
      "displayName": "SpecificDisease",
      "textExtraction": {
        "score": 0.99955064,
        "textSegment": {
          "startOffset": "19",
          "endOffset": "46",
          "content": "hexosaminidase A deficiency"
        }
      }
    },
    {
      "annotationSpecId": "8605379431835369472",
      "displayName": "SpecificDisease",
      "textExtraction": {
        "score": 0.9995449,
        "textSegment": {
          "startOffset": "149",
          "endOffset": "166",
          "content": "Tay-Sachs disease"
        }
      }
    },
    {
      "annotationSpecId": "8605379431835369472",
      "displayName": "SpecificDisease",
      "textExtraction": {
        "score": 0.99939877,
        "textSegment": {
          "startOffset": "169",
          "endOffset": "172",
          "content": "TSD"
        }
      }
    },
    {
      "annotationSpecId": "3417232661104558080",
      "displayName": "Modifier",
      "textExtraction": {
        "score": 0.9993252,
        "textSegment": {
          "startOffset": "236",
          "endOffset": "239",
          "content": "TSD"
        }
      }
    },
    {
      "annotationSpecId": "3417232661104558080",
      "displayName": "Modifier",
      "textExtraction": {
        "score": 0.9993484,
        "textSegment": {
          "startOffset": "330",
          "endOffset": "333",
          "content": "TSD"
        }
      }
    },
    {
      "annotationSpecId": "3417232661104558080",
      "displayName": "Modifier",
      "textExtraction": {
        "score": 0.9993844,
        "textSegment": {
          "startOffset": "688",
          "endOffset": "691",
          "content": "TSD"
        }
      }
    }
  ]
}
Cleaning up
To clean up all GCP resources used in this project, you can delete the GCP
project you used for the tutorial.
Otherwise, you can delete the individual resources you created in this tutorial.
End of explanation
<END_TASK>