doc fixes

src/braket/jobs/config.py

@@ -63,7 +63,7 @@ class S3DataSourceConfig:
     def __init__(
         self,
         s3_data: str,
-        content_type: str | None = None,
+        content_type: str = None,
     ):
         """Create a definition for input data used by a Braket Hybrid job.
 

src/braket/jobs/data_persistence.py

@@ -65,9 +65,7 @@ def save_job_checkpoint(
         f.write(persisted_data.json())
 
 
-def load_job_checkpoint(
-    job_name: str | None = None, checkpoint_file_suffix: str = ""
-) -> dict[str, Any]:
+def load_job_checkpoint(job_name: str = None, checkpoint_file_suffix: str = "") -> dict[str, Any]:
     """
     Loads the job checkpoint data stored for the job named 'job_name', with the checkpoint
     file that ends with the `checkpoint_file_suffix`. The `job_name` can refer to any job whose
@@ -80,7 +78,7 @@ def load_job_checkpoint(
 
 
     Args:
-        job_name (str | None): str that specifies the name of the job whose checkpoints
+        job_name (str): str that specifies the name of the job whose checkpoints
             are to be loaded. Default: current job name.
 
         checkpoint_file_suffix (str): str specifying the file suffix that is used to

src/braket/jobs/hybrid_job.py

@@ -25,7 +25,7 @@
 from logging import Logger, getLogger
 from pathlib import Path
 from types import ModuleType
-from typing import Any
+from typing import Any, Dict, List
 
 import cloudpickle
 
@@ -45,22 +45,22 @@
 
 def hybrid_job(
     *,
-    device: str | None,
+    device: str,
     include_modules: str | ModuleType | Iterable[str | ModuleType] = None,
-    dependencies: str | Path | None = None,
+    dependencies: str | Path = None,
     local: bool = False,
-    job_name: str | None = None,
-    image_uri: str | None = None,
+    job_name: str = None,
+    image_uri: str = None,
     input_data: str | dict | S3DataSourceConfig = None,
     wait_until_complete: bool = False,
-    instance_config: InstanceConfig | None = None,
-    distribution: str | None = None,
-    copy_checkpoints_from_job: str | None = None,
-    checkpoint_config: CheckpointConfig | None = None,
-    role_arn: str | None = None,
-    stopping_condition: StoppingCondition | None = None,
-    output_data_config: OutputDataConfig | None = None,
-    aws_session: AwsSession | None = None,
+    instance_config: InstanceConfig = None,
+    distribution: str = None,
+    copy_checkpoints_from_job: str = None,
+    checkpoint_config: CheckpointConfig = None,
+    role_arn: str = None,
+    stopping_condition: StoppingCondition = None,
+    output_data_config: OutputDataConfig = None,
+    aws_session: AwsSession = None,
     tags: dict[str, str] = None,
     logger: Logger = getLogger(__name__),
 ) -> Callable:
@@ -73,7 +73,7 @@ def hybrid_job(
     `copy_checkpoints_from_job`, `stopping_condition`, `tags`, and `logger`.
 
     Args:
-        device (str | None): Device ARN of the QPU device that receives priority quantum
+        device (str): Device ARN of the QPU device that receives priority quantum
             task queueing once the hybrid job begins running. Each QPU has a separate hybrid jobs
             queue so that only one hybrid job is running at a time. The device string is accessible
             in the hybrid job instance as the environment variable "AMZN_BRAKET_DEVICE_ARN".
@@ -85,20 +85,20 @@ def hybrid_job(
             modules to be included. Any references to members of these modules in the hybrid job
             algorithm code will be serialized as part of the algorithm code. Default value `[]`
 
-        dependencies (str | Path | None): Path (absolute or relative) to a requirements.txt
+        dependencies (str | Path): Path (absolute or relative) to a requirements.txt
             file to be used for the hybrid job.
 
         local (bool): Whether to use local mode for the hybrid job. Default `False`
 
-        job_name (str | None): A string that specifies the name with which the job is created.
+        job_name (str): A string that specifies the name with which the job is created.
             Allowed pattern for job name: `^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,50}$`. Defaults to
             f'{decorated-function-name}-{timestamp}'.
 
-        image_uri (str | None): A str that specifies the ECR image to use for executing the job.
+        image_uri (str): A str that specifies the ECR image to use for executing the job.
             `retrieve_image()` function may be used for retrieving the ECR image URIs
             for the containers supported by Braket. Default = `<Braket base image_uri>`.
 
-        input_data (str | Dict | S3DataSourceConfig): Information about the training
+        input_data (str | dict | S3DataSourceConfig): Information about the training
             data. Dictionary maps channel names to local paths or S3 URIs. Contents found
             at any local paths will be uploaded to S3 at
             f's3://{default_bucket_name}/jobs/{job_name}/data/{channel_name}. If a local
@@ -110,51 +110,59 @@ def hybrid_job(
             This would tail the job logs as it waits. Otherwise `False`. Ignored if using
             local mode. Default: `False`.
 
-        instance_config (InstanceConfig | None): Configuration of the instance(s) for running the
+        instance_config (InstanceConfig): Configuration of the instance(s) for running the
             classical code for the hybrid job. Defaults to
             `InstanceConfig(instanceType='ml.m5.large', instanceCount=1, volumeSizeInGB=30)`.
 
-        distribution (str | None): A str that specifies how the job should be distributed.
+        distribution (str): A str that specifies how the job should be distributed.
             If set to "data_parallel", the hyperparameters for the job will be set to use data
             parallelism features for PyTorch or TensorFlow. Default: None.
 
-        copy_checkpoints_from_job (str | None): A str that specifies the job ARN whose
+        copy_checkpoints_from_job (str): A str that specifies the job ARN whose
             checkpoint you want to use in the current job. Specifying this value will copy
             over the checkpoint data from `use_checkpoints_from_job`'s checkpoint_config
             s3Uri to the current job's checkpoint_config s3Uri, making it available at
             checkpoint_config.localPath during the job execution. Default: None
 
-        checkpoint_config (CheckpointConfig | None): Configuration that specifies the
+        checkpoint_config (CheckpointConfig): Configuration that specifies the
             location where checkpoint data is stored.
             Default: CheckpointConfig(localPath='/opt/jobs/checkpoints',
             s3Uri=f's3://{default_bucket_name}/jobs/{job_name}/checkpoints').
 
-        role_arn (str | None): A str providing the IAM role ARN used to execute the
+        role_arn (str): A str providing the IAM role ARN used to execute the
             script. Default: IAM role returned by AwsSession's `get_default_jobs_role()`.
 
-        stopping_condition (StoppingCondition | None): The maximum length of time, in seconds,
+        stopping_condition (StoppingCondition): The maximum length of time, in seconds,
             and the maximum number of tasks that a job can run before being forcefully stopped.
             Default: StoppingCondition(maxRuntimeInSeconds=5 * 24 * 60 * 60).
 
-        output_data_config (OutputDataConfig | None): Specifies the location for the output of
+        output_data_config (OutputDataConfig): Specifies the location for the output of
             the job.
             Default: OutputDataConfig(s3Path=f's3://{default_bucket_name}/jobs/{job_name}/data',
             kmsKeyId=None).
 
-        aws_session (AwsSession | None): AwsSession for connecting to AWS Services.
+        aws_session (AwsSession): AwsSession for connecting to AWS Services.
             Default: AwsSession()
 
-        tags (dict[str, str] | None): Dict specifying the key-value pairs for tagging this job.
+        tags (dict[str, str]): Dict specifying the key-value pairs for tagging this job.
             Default: {}.
 
         logger (Logger): Logger object with which to write logs, such as task statuses
             while waiting for task to be in a terminal state. Default is `getLogger(__name__)`
+
+    Returns:
+        Callable: the callable for creating a Hybrid Job.
     """
     _validate_python_version(image_uri, aws_session)
 
-    def _hybrid_job(entry_point):
+    def _hybrid_job(entry_point: Callable) -> Callable:
         @functools.wraps(entry_point)
-        def job_wrapper(*args, **kwargs):
+        def job_wrapper(*args, **kwargs) -> Callable:
+            """
+            The job wrapper.
+            Returns:
+                Callable: the callable for creating a Hybrid Job.
+            """
             with _IncludeModules(include_modules), tempfile.TemporaryDirectory(
                 dir="", prefix="decorator_job_"
             ) as temp_dir:
@@ -208,7 +216,7 @@ def job_wrapper(*args, **kwargs):
     return _hybrid_job
 
 
-def _validate_python_version(image_uri: str | None, aws_session: AwsSession | None = None):
+def _validate_python_version(image_uri: str | None, aws_session: AwsSession | None = None) -> None:
     """Validate python version at job definition time"""
     aws_session = aws_session or AwsSession()
     # user provides a custom image_uri
@@ -257,7 +265,7 @@ def __exit__(self, exc_type, exc_val, exc_tb):
 def _serialize_entry_point(entry_point: Callable, args: tuple, kwargs: dict) -> str:
     """Create an entry point from a function"""
 
-    def wrapped_entry_point():
+    def wrapped_entry_point() -> Any:
         """Partial function wrapping entry point with given parameters"""
         return entry_point(*args, **kwargs)
 
@@ -277,7 +285,7 @@ def wrapped_entry_point():
     )
 
 
-def _log_hyperparameters(entry_point: Callable, args: tuple, kwargs: dict):
+def _log_hyperparameters(entry_point: Callable, args: tuple, kwargs: dict) -> Dict:
     """Capture function arguments as hyperparameters"""
     signature = inspect.signature(entry_point)
     bound_args = signature.bind(*args, **kwargs)
@@ -322,7 +330,7 @@ def _sanitize(hyperparameter: Any) -> str:
     return sanitized
 
 
-def _process_input_data(input_data):
+def _process_input_data(input_data: Dict) -> List[str]:
     """
     Create symlinks to data
 
@@ -336,12 +344,12 @@ def _process_input_data(input_data):
     if not isinstance(input_data, dict):
         input_data = {"input": input_data}
 
-    def matches(prefix):
+    def matches(prefix: str) -> List[str]:
         return [
             str(path) for path in Path(prefix).parent.iterdir() if str(path).startswith(str(prefix))
         ]
 
-    def is_prefix(path):
+    def is_prefix(path: str) -> bool:
         return len(matches(path)) > 1 or not Path(path).exists()
 
     prefix_channels = set()

src/braket/jobs/image_uris.py

@@ -15,7 +15,7 @@
 import os
 from enum import Enum
 from functools import cache
-from typing import Dict
+from typing import Dict, Set
 
 
 class Framework(str, Enum):
@@ -26,7 +26,7 @@ class Framework(str, Enum):
     PL_PYTORCH = "PL_PYTORCH"
 
 
-def built_in_images(region):
+def built_in_images(region: str) -> Set[str]:
     return {retrieve_image(framework, region) for framework in Framework}
 
 

src/braket/jobs/quantum_job_creation.py

@@ -232,13 +232,13 @@ def prepare_quantum_job(
     return create_job_kwargs
 
 
-def _generate_default_job_name(image_uri: str | None = None, func: Callable | None = None) -> str:
+def _generate_default_job_name(image_uri: str = None, func: Callable = None) -> str:
     """
     Generate default job name using the image uri and entrypoint function.
 
     Args:
-        image_uri (str | None): URI for the image container.
-        func (Callable | None): The entry point function.
+        image_uri (str): URI for the image container.
+        func (Callable): The entry point function.
 
     Returns:
         str: Hybrid job name.
@@ -361,7 +361,7 @@ def _validate_params(dict_arr: dict[str, tuple[any, any]]) -> None:
     Validate that config parameters are of the right type.
 
     Args:
-        dict_arr (Dict[str, Tuple[any, any]]): dict mapping parameter names to
+        dict_arr (dict[str, tuple[any, any]]): dict mapping parameter names to
             a tuple containing the provided value and expected type.
     """
     for parameter_name, value_tuple in dict_arr.items():