Export _ToolInputAccuracyEvaluator from azure.ai.evaluation top-level namespace

Brings _ToolInputAccuracyEvaluator in line with its three sibling tool evaluators (ToolCallAccuracyEvaluator, _ToolCallSuccessEvaluator, _ToolOutputUtilizationEvaluator) which are already exposed on the top-level package. Consumers (notably the Foundry evaluations service catalog) can now import it from azure.ai.evaluation directly instead of reaching into the private _evaluators._tool_input_accuracy submodule.

Author: manaskawale

sdk/evaluation/azure-ai-evaluation/CHANGELOG.md

@@ -5,6 +5,7 @@
 ### Features Added
 
 - Enabled `ToolCallAccuracyEvaluator`, `_ToolInputAccuracyEvaluator`, and `_ToolCallSuccessEvaluator` to run on conversations that include built-in restricted tools (`bing_grounding`, `bing_custom_search`, `azure_ai_search`, `azure_fabric`, `sharepoint_grounding`). These three evaluators grade the agent's tool selection, input arguments, and call status — none of which require the (redacted) tool output body — so the previous unconditional rejection of conversations containing restricted tools is now lifted. Achieved by setting `check_for_unsupported_tools=False` on each evaluator's input validator. `GroundednessEvaluator` and `ToolOutputUtilizationEvaluator` continue to reject restricted tools because they consume the tool output body.
+- Exported `_ToolInputAccuracyEvaluator` from the top-level `azure.ai.evaluation` namespace so consumers no longer need to reach into the private `_evaluators._tool_input_accuracy` submodule. The other tool evaluators were already exposed there; this brings the four siblings in line.
 - `_ToolCallSuccessEvaluator` now deterministically returns `fail` (score `0`, `_passed=False`) without invoking the LLM when any `tool_call` or `tool_result` in the response carries a known-failure `status` (`failed`, `error`, `incomplete`, `cancelled`/`canceled`). This matches the evaluator's binary contract ("FALSE: at least one tool call failed") and prevents the prompty rubric -- which doesn't see the `status` field -- from mis-grading conversations whose only failure signal is the runtime-reported execution status. Behavior is unchanged for responses where no `status` is populated.
 
 ## 1.17.0 (2026-06-03)

sdk/evaluation/azure-ai-evaluation/azure/ai/evaluation/__init__.py

@@ -34,6 +34,7 @@
 from ._evaluators._document_retrieval import DocumentRetrievalEvaluator
 from ._evaluators._tool_output_utilization import _ToolOutputUtilizationEvaluator
 from ._evaluators._tool_call_success import _ToolCallSuccessEvaluator
+from ._evaluators._tool_input_accuracy import _ToolInputAccuracyEvaluator
 from ._model_configurations import (
     AzureAIProject,
     AzureOpenAIModelConfiguration,
@@ -135,6 +136,7 @@ def lazy_import():
     "ToolCallAccuracyEvaluator",
     "_ToolOutputUtilizationEvaluator",
     "_ToolCallSuccessEvaluator",
+    "_ToolInputAccuracyEvaluator",
     "AzureOpenAIGrader",
     "AzureOpenAILabelGrader",
     "AzureOpenAIStringCheckGrader",