### Install Vertex AI SDK (Windows)

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Installs the virtualenv tool and the google-cloud-aiplatform library in a Python virtual environment on Windows systems.

```bash
pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install google-cloud-aiplatform
```

--------------------------------

### Install Vertex AI SDK (Mac/Linux)

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Installs the virtualenv tool and the google-cloud-aiplatform library in a Python virtual environment on Mac/Linux systems.

```bash
pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install google-cloud-aiplatform
```

--------------------------------

### Update a Memory using MemoryBankServiceClient

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Demonstrates how to update a Memory resource using the MemoryBankServiceClient. This example shows the basic structure for making the request and handling the long-running operation. Ensure correct values for request initialization and consider regional endpoints for client creation.

```python
from google.cloud import aiplatform_v1beta1

def sample_update_memory():
    # Create a client
    client = aiplatform_v1beta1.MemoryBankServiceClient()

    # Initialize request argument(s)
    memory = aiplatform_v1beta1.Memory()
    memory.fact = "fact_value"

    request = aiplatform_v1beta1.UpdateMemoryRequest(
        memory=memory,
    )

    # Make the request
    operation = client.update_memory(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)

```

--------------------------------

### Generate Synthetic Data with DataFoundryServiceAsyncClient

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.data_foundry_service.DataFoundryServiceAsyncClient

Use this snippet to generate synthetic data based on a provided task description and output specifications. Ensure the client is initialized with correct regional endpoints if necessary. The example requires modifications for actual use, including valid request parameter values.

```python
# This snippet has been automatically generated and should be regarded as a  
# code template only.  
# It will require modifications to work:  
# - It may require correct/in-range values for request initialization.  
# - It may require specifying regional endpoints when creating the service  
#   client as shown in:  
#   https://googleapis.dev/python/google-api-core/latest/client_options.html  
from google.cloud import aiplatform_v1  
  
async def sample_generate_synthetic_data():  
    # Create a client  
    client = aiplatform_v1.DataFoundryServiceAsyncClient()  

    # Initialize request argument(s)  
    task_description = aiplatform_v1.TaskDescriptionStrategy()  
    task_description.task_description = "task_description_value"  

    output_field_specs = aiplatform_v1.OutputFieldSpec()  
    output_field_specs.field_name = "field_name_value"  

    request = aiplatform_v1.GenerateSyntheticDataRequest(  
        task_description=task_description,  
        location="location_value",  
        count=553,  
        output_field_specs=output_field_specs,  
    )  

    # Make the request  
    response = await client.generate_synthetic_data(request=request)  

    # Handle the response  
    print(response)  

```

--------------------------------

### Get Predictions from Endpoint

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Send instances to an endpoint to get predictions. The instances should be in a format compatible with the deployed model.

```python
endpoint.predict(instances=[[6.7, 3.1, 4.7, 1.5], [4.6, 3.1, 1.5, 0.2]])
```

--------------------------------

### Initialize Client from Service Account Info

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Creates an instance of MemoryBankServiceClient using provided service account credentials in dictionary format. Pass the dictionary containing the credentials.

```python
from_service_account_info(info: dict, *args, **kwargs)
```

--------------------------------

### SystemLabelsEntry Initialization

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.DeployRequest.DeployConfig.SystemLabelsEntry

Demonstrates how to initialize a SystemLabelsEntry object with various parameters.

```APIDOC
## SystemLabelsEntry

### Description

The abstract base class for a message. It can be initialized with keyword arguments, a mapping, or both. An option to ignore unknown fields is also available.

### Parameters

- **kwargs** (dict) - Keys and values corresponding to the fields of the message.
- **mapping** (Union[dict, `.Message`]) - A dictionary or message to be used to determine the values for this message.
- **ignore_unknown_fields** (Optional[bool]) - If True, do not raise errors for unknown fields. Only applied if `mapping` is a mapping type or there are keyword parameters.

### Example

```python
# Example using keyword arguments
system_labels_entry = SystemLabelsEntry(key1='value1', key2='value2')

# Example using a mapping
mapping_data = {'key3': 'value3'}
system_labels_entry_from_mapping = SystemLabelsEntry(mapping=mapping_data)

# Example ignoring unknown fields
system_labels_entry_ignore = SystemLabelsEntry(unknown_field='some_value', ignore_unknown_fields=True)
```
```

--------------------------------

### Initialize Client from Service Account File

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Creates an instance of MemoryBankServiceClient using credentials from a service account JSON file. Specify the path to the credentials file.

```python
from_service_account_file(filename: str, *args, **kwargs)
```

--------------------------------

### Initializing the SDK

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Initialize the Vertex AI SDK with your project details, region, and other configurations.

```APIDOC
aiplatform.init(
    project='my-project',
    location='us-central1',
    staging_bucket='gs://my_staging_bucket',
    credentials=my_credentials,
    encryption_spec_key_name=my_encryption_key_name,
    experiment='my-experiment',
    experiment_description='my experiment description'
)
```

--------------------------------

### get_location

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.data_foundry_service.DataFoundryServiceAsyncClient

Gets information about a location.

```APIDOC
## get_location

### Description
Gets information about a location.

### Method
get_location

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
None

### Response
#### Success Response (200)
- **Location** (google.cloud.location.locations_pb2.Location) - Location object.

#### Response Example
None
```

--------------------------------

### Get Operation

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Retrieves the latest state of a long-running operation.

```APIDOC
## GET OPERATION

### Description
Gets the latest state of a long-running operation.

### Method
GET

### Endpoint
`/operations/{operation_id}`

### Parameters
#### Path Parameters
- **operation_id** (string) - Required - The ID of the operation to retrieve.

#### Query Parameters
- **retry** (google.api_core.retry.Retry) - Optional - Designation of what errors, if any, should be retried.
- **timeout** (float) - Optional - The timeout for this request.
- **metadata** (Sequence[Tuple[str, Union[str, bytes]]]) - Optional - Key/value pairs which should be sent along with the request as metadata.

### Response
#### Success Response (200)
- **Operation** (google.longrunning.operations_pb2.Operation) - An Operation object.

#### Response Example
{
  "done": false,
  "name": "operations/12345",
  "response": {}
}
```

--------------------------------

### Initialize KNN Configuration

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.RagVectorDbConfig.RagManagedDb.KNN

Instantiate the KNN configuration object. This is used to set up parameters for KNN search.

```python
KNN(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### Get Model

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Retrieves a model resource using its resource name.

```APIDOC
## Get Model

### Description
Retrieves a model resource by its resource name.

### Method
`aiplatform.Model`

### Parameters
- **model_name** (string) - Required - The resource name of the model (e.g., 'projects/my-project/locations/us-central1/models/{MODEL_ID}').

### Request Example
```python
model = aiplatform.Model('projects/my-project/locations/us-central1/models/{MODEL_ID}')
```
```

--------------------------------

### Get Predictions from Endpoint

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Retrieves predictions from a deployed model on an endpoint using a list of instances.

```APIDOC
## Get Predictions from Endpoint

To get predictions from endpoints:
```python
endpoint.predict(instances=[[6.7, 3.1, 4.7, 1.5], [4.6, 3.1, 1.5, 0.2]])
```
```

--------------------------------

### Initialize ResourceLimitsEntry

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.ReasoningEngineSpec.DeploymentSpec.ResourceLimitsEntry

Instantiate ResourceLimitsEntry with optional mapping and ignore_unknown_fields parameters. Use mapping for initial values or keyword arguments for direct field assignment.

```python
ResourceLimitsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### Get Operation

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Retrieves the latest status of a long-running operation. Use this method to check the progress or result of an asynchronous task.

```python
get_operation(
    request: typing.Optional[
        google.longrunning.operations_pb2.GetOperationRequest
    ] = None,
    *, 
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, typing.Union[str, bytes]]] = ()
) -> google.longrunning.operations_pb2.Operation
```

--------------------------------

### Get Memory

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Retrieves a specific memory resource using its name. This method allows you to fetch details about a stored memory.

```APIDOC
## get_memory

### Description
Get a Memory.

### Method
`get_memory`

### Parameters
#### Request Body
- **request** (Union[google.cloud.aiplatform_v1beta1.types.GetMemoryRequest, dict]) - Optional. The request object. Request message for MemoryBankService.GetMemory.
- **name** (str) - Optional. Required. The resource name of the Memory. Format: `projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/memories/{memory}`. This corresponds to the `name` field on the `request` instance; if `request` is provided, this should not be set.
- **retry** (google.api_core.retry.Retry) - Optional. Designation of what errors, if any, should be retried.
- **timeout** (float) - Optional. The timeout for this request.
- **metadata** (Sequence[Tuple[str, Union[str, bytes]]]) - Optional. Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`.

### Response
#### Success Response (200)
- **Memory** (google.cloud.aiplatform_v1beta1.types.Memory) - A memory.

### Request Example
```python
# This snippet has been automatically generated and should be regarded as a  
# code template only.  
# It will require modifications to work:  
# - It may require correct/in-range values for request initialization.  
# - It may require specifying regional endpoints when creating the service  
#   client as shown in:  
#   https://googleapis.dev/python/google-api-core/latest/client_options.html  
from google.cloud import aiplatform_v1beta1  
  
def sample_get_memory():  
    # Create a client  
    client = aiplatform_v1beta1.MemoryBankServiceClient()  
  
    # Initialize request argument(s)  
    request = aiplatform_v1beta1.GetMemoryRequest(  
        name="name_value",  
    )  
  
    # Make the request  
    response = client.get_memory(request=request)  
  
    # Handle the response  
    print(response)  
```
```

--------------------------------

### Instantiate ReviewSnippet

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.GroundingChunk.Maps.PlaceAnswerSources.ReviewSnippet

Demonstrates how to instantiate the ReviewSnippet class. Use this to create a new ReviewSnippet object, optionally providing a mapping and specifying whether to ignore unknown fields.

```python
ReviewSnippet(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### get_iam_policy

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.data_foundry_service.DataFoundryServiceAsyncClient

Gets the IAM access control policy for a function. Returns an empty policy if the function exists and does not have a policy set.

```APIDOC
## get_iam_policy

### Description
Gets the IAM access control policy for a function. Returns an empty policy if the function exists and does not have a policy set.

### Method
get_iam_policy

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
None

### Response
#### Success Response (200)
- **Policy** (google.iam.v1.policy_pb2.Policy) - Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A `Policy` is a collection of `bindings`. A `binding` binds one or more `members` to a single `role`. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A `role` is a named list of permissions (defined by IAM or configured by users). A `binding` can optionally specify a `condition`, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource.

#### Response Example
```json
{
  "bindings": [
    {
      "role": "roles/resourcemanager.organizationAdmin",
      "members": [
        "user:mike@example.com",
        "group:admins@example.com",
        "domain:google.com",
        "serviceAccount:my-project-id@appspot.gserviceaccount.com"
      ]
    },
    {
      "role": "roles/resourcemanager.organizationViewer",
      "members": [
        "user:eve@example.com"
      ],
      "condition": {
        "title": "expirable access",
        "description": "Does not grant access after Sep 2020",
        "expression": "request.time < timestamp('2020-10-01t00:00:00.000z')"
      }
    }
  ]
}
```
```

--------------------------------

### List Memories using MemoryBankServiceClient

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Demonstrates how to list memories using the MemoryBankServiceClient. Ensure the client is initialized and a valid parent resource name is provided.

```python
from google.cloud import aiplatform_v1beta1

def sample_list_memories():
    # Create a client
    client = aiplatform_v1beta1.MemoryBankServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListMemoriesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_memories(request=request)

    # Handle the response
    for response in page_result:
        print(response)
```

--------------------------------

### Initialize Client from Service Account JSON File (Alias)

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Creates an instance of MemoryBankServiceClient using credentials from a service account JSON file. This is an alternative method for initializing the client with file-based credentials.

```python
from_service_account_json(filename: str, *args, **kwargs)
```

--------------------------------

### Get Explainable AI Metadata (TensorFlow 2)

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Retrieves explainable AI metadata from TensorFlow 2 models in dictionary format.

```APIDOC
## Explainable AI: Get Metadata (TensorFlow 2)

To get metadata in dictionary format from TensorFlow 2 models:
```python
from google.cloud.aiplatform.explain.metadata.tf.v2 import saved_model_metadata_builder

builder = saved_model_metadata_builder.SavedModelMetadataBuilder('gs://python/to/my/model/dir')
generated_md = builder.get_metadata()
```
```

--------------------------------

### Create a Memory using MemoryBankServiceClient

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Demonstrates how to create a Memory resource using the MemoryBankServiceClient. Ensure correct values for request initialization and consider specifying regional endpoints.

```python
from google.cloud import aiplatform_v1beta1

def sample_create_memory():
    # Create a client
    client = aiplatform_v1beta1.MemoryBankServiceClient()

    # Initialize request argument(s)
    memory = aiplatform_v1beta1.Memory()
    memory.fact = "fact_value"

    request = aiplatform_v1beta1.CreateMemoryRequest(
        parent="parent_value",
        memory=memory,
    )

    # Make the request
    operation = client.create_memory(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
```

--------------------------------

### Get Explainable AI Metadata (TensorFlow 1)

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Retrieves explainable AI metadata from TensorFlow 1 models in dictionary format.

```APIDOC
## Explainable AI: Get Metadata (TensorFlow 1)

To get metadata in dictionary format from TensorFlow 1 models:
```python
from google.cloud.aiplatform.explain.metadata.tf.v1 import saved_model_metadata_builder

builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
        'gs://python/to/my/model/dir', tags=[tf.saved_model.tag_constants.SERVING]
      )
generated_md = builder.get_metadata()
```
```

--------------------------------

### Deploy Model with Explanation Metadata

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Deploy a model to an endpoint, including explanation metadata. This allows for generating explanations for predictions.

```python
explanation_metadata = builder.get_metadata_protobuf()

# To deploy a model to an endpoint with explanation
model.deploy(..., explanation_metadata=explanation_metadata)

# To deploy a model to a created endpoint with explanation
endpoint.deploy(..., explanation_metadata=explanation_metadata)
```

--------------------------------

### Get TensorFlow 2 SavedModel Metadata

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Build metadata for TensorFlow 2 SavedModel in dictionary format. Specify the GCS path.

```python
from google.cloud.aiplatform.explain.metadata.tf.v2 import saved_model_metadata_builder

builder = saved_model_metadata_builder.SavedModelMetadataBuilder('gs://python/to/my/model/dir')
generated_md = builder.get_metadata()
```

--------------------------------

### DataFoundryServiceAsyncClient

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.data_foundry_service.DataFoundryServiceAsyncClient

Initializes the DataFoundryServiceAsyncClient.

```APIDOC
## DataFoundryServiceAsyncClient

### Description
Initializes the DataFoundryServiceAsyncClient. This client is used for organizing, saving, and categorizing content based on user preferences, and for generating and preparing datasets for Gen AI evaluation.

### Parameters
* **credentials** (google.auth.credentials.Credentials, optional): The `Credentials` to use for this client. If not specified, the client will attempt to determine credentials automatically.
* **transport** (typing.Union[str, google.cloud.aiplatform_v1.services.data_foundry_service.transports.base.DataFoundryServiceTransport, typing.Callable[..., google.cloud.aiplatform_v1.services.data_foundry_service.transports.base.DataFoundryServiceTransport]], optional): The transport to use for the API. Defaults to `grpc_asyncio`.
* **client_options** (google.api_core.client_options.ClientOptions, optional): Client options for the client. If not specified, default client options will be used.
* **client_info** (google.api_core.gapic_v1.client_info.ClientInfo, optional): Client information for the client. If not specified, default client information will be used.
```

--------------------------------

### Cancel Operation

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Starts asynchronous cancellation on a long-running operation. Success is not guaranteed, and the server may return `google.rpc.Code.UNIMPLEMENTED` if it doesn't support this method.

```python
cancel_operation(
    request: typing.Optional[
        google.longrunning.operations_pb2.CancelOperationRequest
    ] = None,
    *, 
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, typing.Union[str, bytes]]] = ()
) -> None
```

--------------------------------

### SystemLabelsEntry Constructor

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.DeployRequest.DeployConfig.SystemLabelsEntry

Initializes a new instance of the SystemLabelsEntry class.

```APIDOC
## SystemLabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

### Description
Initializes a new instance of the SystemLabelsEntry class.

### Parameters
#### Parameters
- **kwargs** (dict) - Required/Optional - Keys and values corresponding to the fields of the message.
- **mapping** (Union[dict, `.Message`]) - Required/Optional - A dictionary or message to be used to determine the values for this message.
- **ignore_unknown_fields** (Optional[bool]) - Required/Optional - If True, do not raise errors for unknown fields. Only applied if `mapping` is a mapping type or there are keyword parameters.
```

--------------------------------

### Get TensorFlow 1 SavedModel Metadata

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Build metadata for TensorFlow 1 SavedModel in dictionary format. Specify the GCS path and serving tags.

```python
from google.cloud.aiplatform.explain.metadata.tf.v1 import saved_model_metadata_builder

builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
        'gs://python/to/my/model/dir', tags=[tf.saved_model.tag_constants.SERVING]
      )
generated_md = builder.get_metadata()
```

--------------------------------

### Get Model Evaluation

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Retrieves a specific model evaluation resource for a given model. It can return the first evaluation by default or a specific evaluation if an ID is provided.

```APIDOC
## Get Model Evaluation

### Description
Retrieves a model evaluation resource.

### Method
`model.get_model_evaluation`

### Parameters
- **evaluation_id** (string) - Optional - The ID of the specific model evaluation to retrieve.

### Request Example
```python
model = aiplatform.Model('projects/my-project/locations/us-central1/models/{MODEL_ID}')

# Returns the first evaluation with no arguments
evaluation = model.get_model_evaluation()

# To get a specific evaluation by ID:
# evaluation = model.get_model_evaluation(evaluation_id='{EVALUATION_ID}')

eval_metrics = evaluation.metrics
```
```

--------------------------------

### Create and Run Vertex AI Pipeline

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Instantiate a PipelineJob and execute it synchronously, monitoring until completion. Configure pipeline parameters, template path, root, and service account.

```python
# Instantiate PipelineJob object
pl = PipelineJob(
    display_name="My first pipeline",

    # Whether or not to enable caching
    # True = always cache pipeline step result
    # False = never cache pipeline step result
    # None = defer to cache option for each pipeline component in the pipeline definition
    enable_caching=False,

    # Local or GCS path to a compiled pipeline definition
    template_path="pipeline.json",

    # Dictionary containing input parameters for your pipeline
    parameter_values=parameter_values,

    # GCS path to act as the pipeline root
    pipeline_root=pipeline_root,
)

# Execute pipeline in Vertex AI and monitor until completion
pl.run(
  # Email address of service account to use for the pipeline run
  # You must have iam.serviceAccounts.actAs permission on the service account to use it
  service_account=service_account,

  # Whether this function call should be synchronous (wait for pipeline run to finish before terminating)
  # or asynchronous (return immediately)
  sync=True
)
```

--------------------------------

### LabelsEntry Initialization

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.PredictRequest.LabelsEntry

Demonstrates how to initialize a LabelsEntry object with various parameters.

```APIDOC
## LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

### Description
Initializes a LabelsEntry object. This is an abstract base class for messages used to stay organized with collections and save/categorize content based on preferences.

### Parameters
- **kwargs** (dict) - Optional. Keys and values corresponding to the fields of the message.
- **mapping** (Union[dict, `.Message`]) - Optional. A dictionary or message to be used to determine the values for this message.
- **ignore_unknown_fields** (Optional[bool]) - Optional. If True, do not raise errors for unknown fields. Only applied if `mapping` is a mapping type or there are keyword parameters.
```

--------------------------------

### Get Vertex AI Model

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Retrieves a Vertex AI Model resource using its resource name. This allows for subsequent operations like deployment or export.

```python
model = aiplatform.Model('/projects/my-project/locations/us-central1/models/{MODEL_ID}')
```

--------------------------------

### Get Existing Image Dataset

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Retrieves a previously created Vertex AI Image Dataset using its resource name. Replace '{DATASET_ID}' with the actual ID of your dataset.

```python
dataset = aiplatform.ImageDataset('projects/my-project/location/us-central1/datasets/{DATASET_ID}')
```

--------------------------------

### Common Project Path

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Returns a fully-qualified project string.

```python
common_project_path(project: str) -> str
```

--------------------------------

### Initialize Vertex AI SDK

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Initializes the Vertex AI SDK with common configurations such as project ID, region, and staging bucket. Ensure your Google Cloud Project ID, Vertex AI region, and a Cloud Storage bucket are correctly specified.

```python
aiplatform.init(
    # your Google Cloud Project ID or number
    # environment default used is not set
    project='my-project',

    # the Vertex AI region you will use
    # defaults to us-central1
    location='us-central1',

    # Google Cloud Storage bucket in same region as location
    # used to stage artifacts
    staging_bucket='gs://my_staging_bucket',

    # custom google.auth.credentials.Credentials
    # environment default credentials used if not set
    credentials=my_credentials,

    # customer managed encryption key resource name
    # will be applied to all Vertex AI resources if set
    encryption_spec_key_name=my_encryption_key_name,

    # the name of the experiment to use to track
    # logged metrics and parameters
    experiment='my-experiment',

    # description of the experiment above
    experiment_description='my experiment description'
)
```

--------------------------------

### cancel_operation

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.data_foundry_service.DataFoundryServiceAsyncClient

Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

```APIDOC
## cancel_operation

### Description
Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`.

### Method
`cancel_operation`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **request** (`google.longrunning.operations_pb2.CancelOperationRequest`, optional) - The request object. Request message for `CancelOperation` method.
- **retry** (`google.api_core.retry.retry_unary_async.AsyncRetry` or `google.api_core.gapic_v1.method._MethodDefault`, optional) - Designation of what errors, if any, should be retried.
- **timeout** (`float` or `object`, optional) - The timeout for this request.
- **metadata** (`Sequence[Tuple[str, Union[str, bytes]]]`, optional) - Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`.
```

--------------------------------

### from_service_account_file

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.data_foundry_service.DataFoundryServiceAsyncClient

Creates an instance of this client using the provided credentials file.

```APIDOC
## from_service_account_file

### Description
Creates an instance of this client using the provided credentials file.

### Method
`from_service_account_file`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **filename** (`str`) - The path to the service account private key json file.
- **args** - Additional positional arguments.
- **kwargs** - Additional keyword arguments.

### Returns
- **Type**: `DataFoundryServiceAsyncClient`
- **Description**: The constructed client.
```

--------------------------------

### Get Model Evaluation

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Retrieves a specific model evaluation resource for a given Vertex AI Model. By default, it returns the first evaluation. An evaluation ID can be provided to fetch a specific one.

```python
model = aiplatform.Model('projects/my-project/locations/us-central1/models/{MODEL_ID}')

# returns the first evaluation with no arguments, you can also pass the evaluation ID
evaluation = model.get_model_evaluation()


eval_metrics = evaluation.metrics
```

--------------------------------

### Recommendation Constructor

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.RecommendSpecResponse.Recommendation

Initializes a Recommendation object. This object represents a recommendation for a deployment option based on custom weights for a model in a specific region. It includes machine and container specifications, as well as the user's accelerator quota status.

```APIDOC
## Recommendation

### Description
Represents a recommendation of one deployment option for a given custom weights model in one region. Contains the machine and container spec, and user accelerator quota state.

### Method Signature
```python
Recommendation(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

### Parameters
* `mapping` (Optional): A mapping for the recommendation.
* `ignore_unknown_fields` (Optional, bool): If true, unknown fields will be ignored during deserialization.
* `**kwargs` (Optional): Additional keyword arguments.
```

--------------------------------

### Get a Memory using MemoryBankServiceClient

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Retrieves a specific Memory resource by its name. Ensure the client is initialized with appropriate credentials and regional endpoints if necessary. The `name` parameter requires a fully qualified resource name.

```python
from google.cloud import aiplatform_v1beta1

def sample_get_memory():
    # Create a client
    client = aiplatform_v1beta1.MemoryBankServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetMemoryRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_memory(request=request)

    # Handle the response
    print(response)
```

--------------------------------

### from_service_account_info

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.data_foundry_service.DataFoundryServiceAsyncClient

Creates an instance of this client using the provided credentials info.

```APIDOC
## from_service_account_info

### Description
Creates an instance of this client using the provided credentials info.

### Method
`from_service_account_info`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **info** (`dict`) - The service account private key info.
- **args** - Additional positional arguments.
- **kwargs** - Additional keyword arguments.

### Returns
- **Type**: `DataFoundryServiceAsyncClient`
- **Description**: The constructed client.
```

--------------------------------

### Unprovisioned

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.RagManagedDbConfig.Unprovisioned

Disables the RAG Engine service and deletes all your data held within this service. This will halt the billing of the service. NOTE: Once deleted the data cannot be recovered. To start using RAG Engine again, you will need to update the tier by calling the UpdateRagEngineConfig API.

```APIDOC
## Unprovisioned

### Description
Disables the RAG Engine service and deletes all your data held within this service. This will halt the billing of the service. Once deleted the data cannot be recovered. To start using RAG Engine again, you will need to update the tier by calling the UpdateRagEngineConfig API.

### Method Signature
```python
Unprovisioned(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```
```

--------------------------------

### Instantiate DeploymentTier

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.DeployedIndex.DeploymentTier

Instantiate a DeploymentTier object by providing its value. This is useful for configuring deployment settings.

```python
DeploymentTier(value)
```

--------------------------------

### SystemLabelsEntry Constructor

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.DeployRequest.DeployConfig.SystemLabelsEntry

Initializes a new instance of the SystemLabelsEntry class. It accepts keyword arguments for message fields, an optional mapping for initial values, and a flag to ignore unknown fields.

```APIDOC
## Class SystemLabelsEntry

### Description

The abstract base class for a message. Stay organized with collections. Save and categorize content based on your preferences.

### Parameters

*   **kwargs** (`dict`) - Keys and values corresponding to the fields of the message.
*   **mapping** (`Union[dict, `.Message`]`)
    A dictionary or message to be used to determine the values for this message.
*   **ignore_unknown_fields** (`Optional[bool]`)
    If True, do not raise errors for unknown fields. Only applied if `mapping` is a mapping type or there are keyword parameters.

### Methods

(No specific methods documented in the provided text)
```

--------------------------------

### Initialize Recommendation

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.RecommendSpecResponse.Recommendation

Initializes a Recommendation object. This is typically used internally by the SDK.

```python
Recommendation(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### ScopeEntry Initialization

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.Memory.ScopeEntry

Demonstrates how to initialize a ScopeEntry object. It can be initialized with keyword arguments, a mapping, or a combination of both. The ignore_unknown_fields parameter can be set to True to prevent errors when unknown fields are present.

```APIDOC
```python
ScopeEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```
```

--------------------------------

### ThinkingConfig Initialization

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.GenerationConfig.ThinkingConfig

Initializes the ThinkingConfig object. Use `ignore_unknown_fields=True` to ignore fields not recognized by the client.

```python
ThinkingConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### Instantiate SourceFlaggingUri

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.GroundingMetadata.SourceFlaggingUri

Instantiates the SourceFlaggingUri class. It accepts optional mapping and keyword arguments, with ignore_unknown_fields defaulting to False.

```python
SourceFlaggingUri(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### Instantiate VeoTuningSpec

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.VeoTuningSpec

Instantiate the VeoTuningSpec class. The `mapping` parameter can be set to None, and `ignore_unknown_fields` can be set to False. Additional keyword arguments can be passed.

```python
VeoTuningSpec(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### Initialize DocumentCorpus

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.RagCorpus.CorpusTypeConfig.DocumentCorpus

Instantiate the DocumentCorpus class to configure document collections. Accepts optional mapping and ignore_unknown_fields parameters.

```python
DocumentCorpus(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### Instantiate UrlContextMetadata

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.UrlContextMetadata

Instantiates the UrlContextMetadata class. Use this to create a new metadata object, optionally providing a mapping and specifying whether to ignore unknown fields.

```python
UrlContextMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### LabelsEntry Constructor

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.PredictRequest.LabelsEntry

This snippet shows how to instantiate the LabelsEntry class with its available parameters.

```APIDOC
## LabelsEntry Constructor

### Description
Instantiates a LabelsEntry object.

### Parameters
- **kwargs** (dict) - Keys and values corresponding to the fields of the message.
- **mapping** (Union[dict, `.Message`]) - A dictionary or message to be used to determine the values for this message.
- **ignore_unknown_fields** (Optional[bool]) - If True, do not raise errors for unknown fields. Only applied if `mapping` is a mapping type or there are keyword parameters.

### Request Example
```python
from google.cloud.aiplatform_v1beta1.types import LabelsEntry

# Example with keyword arguments
entry = LabelsEntry(key='my_key', value='my_value')

# Example with mapping
mapping_data = {'key': 'another_key', 'value': 'another_value'}
entry_from_mapping = LabelsEntry(mapping=mapping_data)

# Example with ignore_unknown_fields
entry_ignore_unknown = LabelsEntry(mapping=mapping_data, ignore_unknown_fields=True)
```

### Response
#### Success Response
An instance of the LabelsEntry class.

### Response Example
```json
{
  "key": "my_key",
  "value": "my_value"
}
```
```

--------------------------------

### Initialize PlaceAnswerSources Class

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.GroundingChunk.Maps

Instantiate the PlaceAnswerSources class, used to detail the sources for a place answer within a Maps chunk. It accepts initialization arguments similar to the Maps class.

```python
PlaceAnswerSources(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### from_service_account_json

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.services.data_foundry_service.DataFoundryServiceAsyncClient

Creates an instance of this client using the provided credentials file.

```APIDOC
## from_service_account_json

### Description
Creates an instance of this client using the provided credentials file.

### Method
`from_service_account_json`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **filename** (`str`) - The path to the service account private key json file.
- **args** - Additional positional arguments.
- **kwargs** - Additional keyword arguments.

### Returns
- **Type**: `DataFoundryServiceAsyncClient`
- **Description**: The constructed client.
```

--------------------------------

### ResourceLimitsEntry Constructor

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.ReasoningEngineSpec.DeploymentSpec.ResourceLimitsEntry

Initializes a new instance of the ResourceLimitsEntry class. It accepts keyword arguments for fields and an optional mapping for initial values.

```APIDOC
## Class ResourceLimitsEntry

### Description
The abstract base class for a message. Stay organized with collections. Save and categorize content based on your preferences.

### Parameters

*   **kwargs** (dict) - Keys and values corresponding to the fields of the message.
*   **mapping** (Union[dict, `.Message`]) - A dictionary or message to be used to determine the values for this message.
*   **ignore_unknown_fields** (Optional[bool]) - If True, do not raise errors for unknown fields. Only applied if `mapping` is a mapping type or there are keyword parameters.

### Methods

(No specific methods are documented in the provided text.)

### Example
```python
ResourceLimitsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```
```

--------------------------------

### Initialize DraftModelSpeculation

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.SpeculativeDecodingSpec.DraftModelSpeculation

Instantiate the DraftModelSpeculation class. The `draft_model` attribute is required and specifies the resource name of the draft model.

```python
DraftModelSpeculation(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### Upload Model with Explanation Metadata

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Use this method to upload a model to Vertex AI, including metadata for explanations. Ensure explanation_metadata is properly defined before calling.

```python
aiplatform.Model.upload(..., explanation_metadata=explanation_metadata)
```

--------------------------------

### LlmParser Initialization

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.RagFileParsingConfig.LlmParser

Initializes the LlmParser with optional mapping and ignore_unknown_fields settings.

```APIDOC
## Class LlmParser

```python
LlmParser(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

Specifies the advanced parsing for RagFiles.
```

--------------------------------

### Creating and Importing a Text Dataset

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Create a text dataset and import data from Google Cloud Storage with a specified schema.

```APIDOC
from google.cloud import aiplatform

my_dataset = aiplatform.TextDataset.create(
    display_name="my-dataset"
)

my_dataset.import_data(
    gcs_source=['gs://path/to/my/dataset.csv'],
    import_schema_uri=aiplatform.schema.dataset.ioformat.text.multi_label_classification
)
```

--------------------------------

### Instantiate DataKeyAndFeatureValues

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.FeatureViewDirectWriteRequest.DataKeyAndFeatureValues

Instantiate DataKeyAndFeatureValues with optional mapping and ignore_unknown_fields parameters.

```python
DataKeyAndFeatureValues(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### ResourceLimitsEntry Constructor

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.ReasoningEngineSpec.DeploymentSpec.ResourceLimitsEntry

Initializes a ResourceLimitsEntry object. It accepts keyword arguments for message fields, an optional mapping for initial values, and a flag to ignore unknown fields.

```APIDOC
## ResourceLimitsEntry

### Description
Initializes a ResourceLimitsEntry object. It accepts keyword arguments for message fields, an optional mapping for initial values, and a flag to ignore unknown fields.

### Parameters

#### Keyword Arguments
- **kwargs** (dict) - Keys and values corresponding to the fields of the message.
- **mapping** (Union[dict, `.Message`]) - A dictionary or message to be used to determine the values for this message.
- **ignore_unknown_fields** (Optional[bool]) - If True, do not raise errors for unknown fields. Only applied if `mapping` is a mapping type or there are keyword parameters.
```

--------------------------------

### Creating a Tabular Dataset

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Create a managed tabular dataset in Vertex AI using data from Google Cloud Storage.

```APIDOC
my_dataset = aiplatform.TabularDataset.create(
    display_name="my-dataset", gcs_source=['gs://path/to/my/dataset.csv']
)
```

--------------------------------

### Create and Import Text Dataset

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Creates a text dataset and then imports data from a specified GCS source. The import schema URI should match the type of text data being imported.

```python
from google.cloud import aiplatform

my_dataset = aiplatform.TextDataset.create(
    display_name="my-dataset")

my_dataset.import_data(
    gcs_source=['gs://path/to/my/dataset.csv'],
    import_schema_uri=aiplatform.schema.dataset.ioformat.text.multi_label_classification
)
```

--------------------------------

### ModelConfig Initialization

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.DeployRequest.ModelConfig

Initializes a ModelConfig object. Use this to set deployment-specific configurations for a model.

```python
ModelConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### Initialize MemoryBankServiceClient

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Instantiates the memory bank service client. Use credentials for authorization and optionally specify transport, client options, or client info. Client options can override the API endpoint and universe domain.

```python
MemoryBankServiceClient(
    *, 
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    transport: typing.Optional[
        typing.Union[
            str,
            google.cloud.aiplatform_v1beta1.services.memory_bank_service.transports.base.MemoryBankServiceTransport,
            typing.Callable[
                [...],
                google.cloud.aiplatform_v1beta1.services.memory_bank_service.transports.base.MemoryBankServiceTransport,
            ],
        ]
    ] = None,
    client_options: typing.Optional[
        typing.Union[google.api_core.client_options.ClientOptions, dict]
    ] = None,
    client_info: google.api_core.gapic_v1.client_info.ClientInfo = google.api_core.gapic_v1.client_info.ClientInfo
)
```

--------------------------------

### BigQuerySourceConfig Constructor

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.ImportIndexRequest.ConnectorConfig

Initializes a BigQuerySourceConfig object. Use this to configure data import from a BigQuery table.

```python
BigQuerySourceConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### ReviewSnippet Constructor

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.GroundingChunk.Maps.PlaceAnswerSources.ReviewSnippet

Initializes a new instance of the ReviewSnippet class. It can optionally take a mapping and a flag to ignore unknown fields.

```APIDOC
## ReviewSnippet

### Description
Encapsulates a review snippet.

### Method Signature
```python
ReviewSnippet(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

### Parameters
* `mapping` (any, optional): An object to map fields from.
* `ignore_unknown_fields` (bool, optional): If True, unknown fields will be ignored. Defaults to False.
* `**kwargs`: Additional keyword arguments.
```

--------------------------------

### Deploy Model to Endpoint

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest

Deploy a model to a created endpoint. Configure replica counts, machine type, and accelerator type/count as needed.

```python
model = aiplatform.Model('/projects/my-project/locations/us-central1/models/{MODEL_ID}')

endpoint.deploy(
    model,
    min_replica_count=1,
    max_replica_count=5,
    machine_type='n1-standard-4',
    accelerator_type='NVIDIA_TESLA_K80',
    accelerator_count=1
)
```

--------------------------------

### MemoryBankServiceClient.from_service_account_file

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Creates an instance of the MemoryBankServiceClient using the provided credentials file.

```APIDOC
### from_service_account_file
```python
from_service_account_file(filename: str, *args, **kwargs)
```

Creates an instance of this client using the provided credentials file.

**Parameter**
---
**Name** | **Description**
`filename` | `str` - The path to the service account private key json file.

**Returns**
---
**Type** | **Description**
`MemoryBankServiceClient` | The constructed client.
```

--------------------------------

### Environment Class Initialization

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.Tool.ComputerUse.Environment

Initializes an Environment object. Use this to represent the environment, such as a web browser.

```python
Environment(value)
```

--------------------------------

### WriteResponse Constructor

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.FeatureViewDirectWriteResponse.WriteResponse

Initializes a new instance of the WriteResponse class.

```APIDOC
## WriteResponse

### Description
Initializes a new instance of the `WriteResponse` class. This class provides details about the write for each key.

### Method
```python
WriteResponse(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

### Parameters
*   **mapping** (Optional) - A mapping of keys to their write details.
*   **ignore_unknown_fields** (Optional, bool) - If true, unknown fields are ignored.
*   **kwargs** - Additional keyword arguments.
```

--------------------------------

### ResourceLimitsEntry Constructor

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.ReasoningEngineSpec.DeploymentSpec.ResourceLimitsEntry

Initializes a new instance of the ResourceLimitsEntry class. It can be initialized with a mapping or keyword arguments.

```APIDOC
## Class ResourceLimitsEntry

### Description

The abstract base class for a message. Stay organized with collections. Save and categorize content based on your preferences.

### Parameters

*   **kwargs** (dict) - Optional. Keys and values corresponding to the fields of the message.
*   **mapping** (Union[dict, `.Message`]) - Optional. A dictionary or message to be used to determine the values for this message.
*   **ignore_unknown_fields** (Optional[bool]) - Optional. If True, do not raise errors for unknown fields. Only applied if `mapping` is a mapping type or there are keyword parameters.

### Methods

(No methods are explicitly documented in the provided source.)

### Example

```python
# Example usage (assuming ResourceLimitsEntry is subclassed or used in a context where it's defined)
# This is a conceptual example as the direct instantiation details are limited in the source.
# from google.cloud.aiplatform_v1beta1.types import ResourceLimitsEntry
# 
# # Example with keyword arguments
# entry_kwargs = ResourceLimitsEntry(key='value', another_field=123)
# 
# # Example with a mapping
# mapping_data = {'key': 'value', 'another_field': 123}
# entry_mapping = ResourceLimitsEntry(mapping=mapping_data)
# 
# # Example with ignore_unknown_fields
# entry_ignore = ResourceLimitsEntry(mapping=mapping_data, ignore_unknown_fields=True)
```
```

--------------------------------

### TuningMode Class Initialization

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.SupervisedTuningSpec.TuningMode

Represents supported tuning modes.

```APIDOC
## Methods
### TuningMode
```
TuningMode(value)
```

Supported tuning modes.
```

--------------------------------

### Instantiate DatapointFieldMapping

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.ImportIndexRequest.ConnectorConfig.DatapointFieldMapping

Instantiate the DatapointFieldMapping class. Use this to define how datapoint fields correspond to columns in your data.

```python
DatapointFieldMapping(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### Instantiate GranularTtlConfig

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.ReasoningEngineContextSpec.MemoryBankConfig.TtlConfig.GranularTtlConfig

Instantiate GranularTtlConfig with optional TTL configurations for different memory actions.

```python
GranularTtlConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```

--------------------------------

### MemoryBankServiceClient.from_service_account_info

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.services.memory_bank_service.MemoryBankServiceClient

Creates an instance of the MemoryBankServiceClient using the provided credentials info.

```APIDOC
### from_service_account_info
```python
from_service_account_info(info: dict, *args, **kwargs)
```

Creates an instance of this client using the provided credentials info.

**Parameter**
---
**Name** | **Description**
`info` | `dict` - The service account private key info.

**Returns**
---
**Type** | **Description**
`MemoryBankServiceClient` | The constructed client.
```

--------------------------------

### Initialize DefsEntry

Source: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1beta1.types.Schema.DefsEntry

Instantiate a DefsEntry object. Use keyword arguments for message fields or a mapping for initial values. Set ignore_unknown_fields to True to prevent errors with unrecognized fields.

```python
DefsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
```