diff --git a/python/pyproject.toml b/python/pyproject.toml
index 87fe6ad82579..238b7e1696d7 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -25,7 +25,7 @@ classifiers = [
 dependencies = [
     # azure agents
     "azure-ai-projects >= 1.0.0b12",
-    "azure-ai-agents >= 1.1.0b4",
+    "azure-ai-agents >= 1.2.0b3",
     "aiohttp ~= 3.8",
     "cloudevents ~=1.0",
     "pydantic >=2.0,!=2.10.0,!=2.10.1,!=2.10.2,!=2.10.3,<2.12",
diff --git a/python/samples/concepts/agents/azure_ai_agent/azure_ai_agent_deep_research_streaming.py b/python/samples/concepts/agents/azure_ai_agent/azure_ai_agent_deep_research_streaming.py
new file mode 100644
index 000000000000..766b2feff3c0
--- /dev/null
+++ b/python/samples/concepts/agents/azure_ai_agent/azure_ai_agent_deep_research_streaming.py
@@ -0,0 +1,155 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from azure.ai.agents.models import DeepResearchTool
+from azure.identity.aio import AzureCliCredential
+
+from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings, AzureAIAgentThread
+from semantic_kernel.contents import (
+    ChatMessageContent,
+    FunctionCallContent,
+    FunctionResultContent,
+    StreamingAnnotationContent,
+)
+
+"""
+The following sample demonstrates how to create an AzureAIAgent along
+with the Deep Research Tool. Please visit the following documentation for more info
+on what is required to run the sample: https://aka.ms/agents-deep-research. Please pay
+attention to the purple `Note` boxes in the Azure docs.
+
+Note that when you use your Bing Connection ID, it needs to be the connection ID from the project, not the resource.
+It has the following format:
+
+'/subscriptions/<sub_id>/resourceGroups/<rg_name>/providers/<provider_name>/accounts/<account_name>/projects/<project_name>/connections/<connection_name>'
+"""
+
+TASK = (
+    "Research the current state of studies on orca intelligence and orca language, "
+    "including what is currently known about orcas' cognitive capabilities and communication systems."
+)
+
+
+async def handle_intermediate_messages(message: ChatMessageContent) -> None:
+    for item in message.items or []:
+        if isinstance(item, FunctionResultContent):
+            print(f"Function Result:> {item.result} for function: {item.name}")
+        elif isinstance(item, FunctionCallContent):
+            print(f"Function Call:> {item.name} with arguments: {item.arguments}")
+        elif isinstance(item, StreamingAnnotationContent):
+            label = item.title or item.url or "Annotation"
+            print(f"Annotation:> {label} ({item.citation_type}) -> {item.url}")
+        else:
+            print(f"{item}")
+
+
+async def main() -> None:
+    async with (
+        AzureCliCredential() as creds,
+        AzureAIAgent.create_client(credential=creds) as client,
+    ):
+        azure_ai_agent_settings = AzureAIAgentSettings()
+        # 1. Define the Deep Research tool
+        deep_research_tool = DeepResearchTool(
+            bing_grounding_connection_id=azure_ai_agent_settings.bing_connection_id,
+            deep_research_model=azure_ai_agent_settings.deep_research_model,
+        )
+
+        # 2. Create an agent with the tool on the Azure AI agent service
+        agent_definition = await client.agents.create_agent(
+            model="gpt-4o",  # Deep Research requires the use of gpt-4o for scope clarification.
+            tools=deep_research_tool.definitions,
+            instructions="You are a helpful Agent that assists in researching scientific topics.",
+        )
+
+        # 3. Create a Semantic Kernel agent for the Azure AI agent
+        agent = AzureAIAgent(client=client, definition=agent_definition, name="DeepResearchAgent")
+
+        # 4. Create a thread for the agent
+        # If no thread is provided, a new thread will be
+        # created and returned with the initial response
+        thread: AzureAIAgentThread | None = None
+
+        try:
+            print(f"# User: '{TASK}'")
+            # 5. Invoke the agent for the specified thread for response
+            first_chunk = True
+            async for response in agent.invoke_stream(
+                messages=TASK,
+                thread=thread,
+                on_intermediate_message=handle_intermediate_messages,
+            ):
+                if first_chunk:
+                    print(f"# {response.name}: ", end="", flush=True)
+                    first_chunk = False
+                # Print the text chunk
+                print(f"{response}", end="", flush=True)
+                # Print any streaming annotations that may arrive in this chunk
+                for item in response.items or []:
+                    if isinstance(item, StreamingAnnotationContent):
+                        label = item.title or item.url or (item.quote or "Annotation")
+                        print(f"\n[Annotation] {label} -> {item.url}")
+                thread = response.thread
+                print()
+        finally:
+            # 6. Cleanup: Delete the thread, agent, and file
+            await thread.delete() if thread else None
+            await client.agents.delete_agent(agent.id)
+
+        """
+        Sample Output:
+        
+        # User: 'Research the current state of studies on orca intelligence and orca language, including what is 
+            currently known about orcas' cognitive capabilities and communication systems.'
+        Function Call:> deep_research with arguments: {'input': '{"prompt": "Research the current state of studies on 
+            orca intelligence and orca communication, focusing on their cognitive capabilities and language systems. 
+            Provide an overview of key discoveries, critical experiments, and major conclusions about their 
+            intelligence and communication systems. Prioritize primary research papers, reputable academic sources, 
+            and recent updates in the field (from the past 5 years if available). Format as a structured report with 
+            appropriate headings for clarity, and respond in English."}'}
+        # azure_agent_QhTQHlUs: Title: Current Studies on Orca Intelligence and Communication
+
+        Starting deep research... 
+
+        The user's task is to research orca intelligence, focusing on cognitive capabilities and communication.
+          【1†Bing Search】
+
+        [Annotation] Bing Search: 'orca communication research 2020 killer whale cognitive study' -> https://www.bing.com/search?q=orca%20communication%20research%202020%20killer%20whale%20cognitive%20study
+
+        **Weighing options**
+
+        I'm examining the research on orca social dynamics, comparing a potential review to a recent journal article 
+        on large-scale unsupervised clustering of orca calls.
+
+        **Investigating orca datasets**
+
+        OK, let me see. PDF, Interspeech 2020, "ORCA-CLEAN: A Deep Denoising Toolkit for Killer Whale Communication" 
+        seems relevant. They focus on cognitive capabilities, language systems, and communication.
+
+        I'm considering if the PDF is relevant and may not need it. ResearchGate's content might need a login 
+        to access. 【1†Bing Search】
+
+        [Annotation] Bing Search: '"Social Dynamics and Intelligence of Killer Whales (Orcinus orca)"' -> https://www.bing.com/search?q=%22Social%20Dynamics%20and%20Intelligence%20of%20Killer%20Whales%20%28Orcinus%20orca%29%22
+
+        **Evaluating sources**
+
+        I'm gathering info on "Social Dynamics and Intelligence of Killer Whales," weighing access to PDFs through 
+        ResearchGate, and considering associated online references for credibility.
+
+        **Considering capabilities**
+
+        I'm piecing together the intricacies of killer whale creativity under chemical stimuli, as explored 
+        in "Manitzas, Hill, et al 2022." Would love to learn more about their findings.
+
+        **Exploring external options**  
+        I'm weighing opening the PDF directly or using an external search. 【1†Bing Search】
+
+        [Annotation] Bing Search: 'Manitzas Hill 2022 killer whale creativity cognitive abilities' -> https://www.bing.com/search?q=Manitzas%20Hill%202022%20killer%20whale%20creativity%20cognitive%20abilities
+        
+        ...
+        """
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/python/samples/getting_started_with_agents/azure_ai_agent/step10_azure_ai_agent_deep_research.py b/python/samples/getting_started_with_agents/azure_ai_agent/step10_azure_ai_agent_deep_research.py
new file mode 100644
index 000000000000..d68758852b36
--- /dev/null
+++ b/python/samples/getting_started_with_agents/azure_ai_agent/step10_azure_ai_agent_deep_research.py
@@ -0,0 +1,143 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from datetime import timedelta
+
+from azure.ai.agents.models import DeepResearchTool
+from azure.identity.aio import AzureCliCredential
+
+from semantic_kernel.agents import AzureAIAgent, AzureAIAgentSettings, AzureAIAgentThread, RunPollingOptions
+from semantic_kernel.contents import AnnotationContent, ChatMessageContent, FunctionCallContent, FunctionResultContent
+
+"""
+The following sample demonstrates how to create an AzureAIAgent along
+with the Deep Research Tool. Please visit the following documentation for more info
+on what is required to run the sample: https://aka.ms/agents-deep-research. Please pay
+attention to the purple `Note` boxes in the Azure docs.
+
+Note that when you use your Bing Connection ID, it needs to be the connection ID from the project, not the resource.
+It has the following format:
+
+'/subscriptions/<sub_id>/resourceGroups/<rg_name>/providers/<provider_name>/accounts/<account_name>/projects/<project_name>/connections/<connection_name>'
+"""
+
+TASK = (
+    "Research the current state of studies on orca intelligence and orca language, "
+    "including what is currently known about orcas' cognitive capabilities and communication systems."
+)
+
+
+async def handle_intermediate_messages(message: ChatMessageContent) -> None:
+    for item in message.items or []:
+        if isinstance(item, FunctionResultContent):
+            print(f"Function Result:> {item.result} for function: {item.name}")
+        elif isinstance(item, FunctionCallContent):
+            print(f"Function Call:> {item.name} with arguments: {item.arguments}")
+        else:
+            print(f"{item}")
+        print()
+
+
+async def main() -> None:
+    async with (
+        AzureCliCredential() as creds,
+        AzureAIAgent.create_client(credential=creds) as client,
+    ):
+        azure_ai_agent_settings = AzureAIAgentSettings()
+        # 1. Define the Deep Research tool
+        deep_research_tool = DeepResearchTool(
+            bing_grounding_connection_id=azure_ai_agent_settings.bing_connection_id,
+            deep_research_model=azure_ai_agent_settings.deep_research_model,
+        )
+
+        # 2. Create an agent with the tool on the Azure AI agent service
+        agent_definition = await client.agents.create_agent(
+            model="gpt-4o",
+            tools=deep_research_tool.definitions,
+            instructions="You are a helpful Agent that assists in researching scientific topics.",
+        )
+
+        # 3. Create a Semantic Kernel agent for the Azure AI agent
+        agent = AzureAIAgent(client=client, definition=agent_definition, name="DeepResearchAgent")
+
+        # 4. Create a thread for the agent
+        # If no thread is provided, a new thread will be
+        # created and returned with the initial response
+        thread: AzureAIAgentThread | None = None
+
+        try:
+            print(f"# User: '{TASK}'")
+            # 5. Invoke the agent for the specified thread for response
+            async for response in agent.invoke(
+                messages=TASK,
+                thread=thread,
+                on_intermediate_message=handle_intermediate_messages,
+                polling_options=RunPollingOptions(run_polling_timeout=timedelta(minutes=10)),
+            ):
+                # Note that the underlying Deep Research Tool uses the o3 reasoning model.
+                # When using the non-streaming invoke, it is normal for there to be
+                # several minutes of processing before agent response(s) appear.
+                # For a fast response, consider using the streaming invoke method.
+                # A sample exists in the `concepts/agents/azure_ai_agent` directory.
+                print(f"# {response.name}: {response}")
+                for item in response.items or []:
+                    if isinstance(item, AnnotationContent):
+                        label = item.title or item.url or (item.quote or "Annotation")
+                        print(f"\n[Annotation] {label} -> {item.url}")
+                thread = response.thread
+        finally:
+            # 6. Cleanup: Delete the thread, agent, and file
+            await thread.delete() if thread else None
+            await client.agents.delete_agent(agent.id)
+
+        """
+        Sample Output:
+        
+        # User: 'Research the current state of studies on orca intelligence and orca language, including what is 
+        currently known about orcas' cognitive capabilities and communication systems.'
+
+        Function Call:> deep_research with arguments: {'input': '{"prompt": "Research the current state of studies on 
+        orca intelligence and orca language. Include up-to-date findings on both their cognitive capabilities and 
+        communication systems. Structure the findings as a detailed report with well-formatted headers that break down 
+        topics such as orcas\' cognitive skills (e.g., problem-solving, memory, and social intelligence), their 
+        language or communication methods (e.g., vocalizations, dialects, and symbolic communication), and comparisons 
+        to other highly intelligent species. Include sources, prioritize recent peer-reviewed studies and reputable 
+        marine biology publications, and ensure the report is well-cited and clear."}'}
+
+        # DeepResearchAgent: Got it! I'll gather recent studies and findings on orca intelligence and their 
+        # communication systems, focusing on cognitive abilities and the mechanisms of their language. 
+        # I'll update you with a comprehensive overview soon.
+
+        Title: Current Research on Orca Intelligence and Language
+
+        Starting deep research... 
+
+        # DeepResearchAgent: cot_summary: **Examining orca intelligence**  
+        The task involves looking into orca cognitive abilities, communication methods, and comparisons with 
+        other intelligent species. It includes recent peer-reviewed studies and credible sources. 【1†Bing Search】
+
+
+        [Annotation] Bing Search: '2024 orca intelligence cognitive study research' -> https://www.bing.com/search?q=2024%20orca%20intelligence%20cognitive%20study%20research
+        # DeepResearchAgent: cot_summary: **Investigating orca intelligence**
+
+        I'm curious about headlines discussing orcas' cognitive abilities and behaviors, especially their tool use, 
+        recent attacks, and comparisons with dolphins. 【1†Bing Search】
+
+
+        [Annotation] Bing Search: 'orca cognition recent peer-reviewed studies orca communication recent study' -> https://www.bing.com/search?q=orca%20cognition%20recent%20peer-reviewed%20studies%20orca%20communication%20recent%20study
+        # DeepResearchAgent: cot_summary: **Researching social dynamics**
+
+        I'm gathering info on killer whale social dynamics from what appears to be a 2024 article on ResearchGate.
+
+        # DeepResearchAgent: cot_summary: **Assessing publication type**  
+        I'm exploring if the ResearchGate link points to a preprint, student paper, or journal article. 
+        Progress is steady as I look into its initiation and source.
+
+        # DeepResearchAgent: cot_summary: **Investigating PDF issues**
+
+        ...
+        """
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/python/semantic_kernel/agents/azure_ai/agent_content_generation.py b/python/semantic_kernel/agents/azure_ai/agent_content_generation.py
index b6ba28dd323d..a2ad2467761d 100644
--- a/python/semantic_kernel/agents/azure_ai/agent_content_generation.py
+++ b/python/semantic_kernel/agents/azure_ai/agent_content_generation.py
@@ -1,5 +1,6 @@
 # Copyright (c) Microsoft. All rights reserved.
 
+import re
 from typing import TYPE_CHECKING, Any, cast
 
 from azure.ai.agents.models import (
@@ -20,6 +21,7 @@
     RunStepAzureAISearchToolCall,
     RunStepBingCustomSearchToolCall,
     RunStepBingGroundingToolCall,
+    RunStepDeepResearchToolCall,
     RunStepDeltaCodeInterpreterImageOutput,
     RunStepDeltaCodeInterpreterLogOutput,
     RunStepDeltaCodeInterpreterToolCall,
@@ -53,6 +55,7 @@
         RunStepDeltaToolCallObject,
     )
 
+_URL_PATTERN = re.compile(r"https?://[^\s\]\)]+", re.IGNORECASE)
 THREAD_MESSAGE_ID = "thread_message_id"
 
 """
@@ -375,6 +378,69 @@ def generate_file_search_content(
     return message_content
 
 
+@experimental
+def generate_deep_research_content(
+    agent_name: str, deep_research_tool_call: "RunStepDeepResearchToolCall"
+) -> ChatMessageContent:
+    """Generate content for a Deep Research tool call.
+
+    Emits both the tool call (input) and the tool result (output). If URLs are present
+    in the output text, a simple "Citations" section with unique URLs is appended as text.
+
+    Args:
+        agent_name: The agent name.
+        deep_research_tool_call: The deep research tool call details.
+
+    Returns:
+        ChatMessageContent summarizing the deep research call and result.
+    """
+    items: list[FunctionCallContent | FunctionResultContent | TextContent] = []
+
+    details = deep_research_tool_call.deep_research
+    # Function call (input)
+    items.append(
+        FunctionCallContent(
+            id=deep_research_tool_call.id,
+            name=deep_research_tool_call.type,
+            function_name=deep_research_tool_call.type,
+            arguments={"input": getattr(details, "input", None)},
+            inner_content=deep_research_tool_call,
+        )
+    )
+
+    # Function result (output)
+    output_text = getattr(details, "output", None)
+    if output_text:
+        items.append(
+            FunctionResultContent(
+                function_name=deep_research_tool_call.type,
+                id=deep_research_tool_call.id,
+                result=output_text,
+                inner_content=deep_research_tool_call,
+            )
+        )
+
+        # Optional: Append a simple citations section from any URLs in the output
+        urls = _extract_unique_urls(str(output_text))
+        if urls:
+            citations_lines = ["## Citations"] + [f"{i + 1}. [{u}]({u})" for i, u in enumerate(urls)]
+            items.append(TextContent(text="\n\n" + "\n".join(citations_lines)))
+
+    return ChatMessageContent(role=AuthorRole.ASSISTANT, name=agent_name, items=items)  # type: ignore
+
+
+def _extract_unique_urls(text: str) -> list[str]:
+    """Extract unique HTTP/HTTPS URLs from text in order of appearance."""
+    seen: set[str] = set()
+    ordered: list[str] = []
+    for match in _URL_PATTERN.finditer(text or ""):
+        url = match.group(0)
+        if url not in seen:
+            seen.add(url)
+            ordered.append(url)
+    return ordered
+
+
 @experimental
 def generate_openapi_content(agent_name: str, openapi_tool_call: RunStepOpenAPIToolCall) -> ChatMessageContent:
     """Generate ChatMessageContent for a non-streaming OpenAPI tool call."""
@@ -577,6 +643,56 @@ def generate_streaming_azure_ai_search_content(
     )  # type: ignore
 
 
+@experimental
+def generate_streaming_deep_research_content(
+    agent_name: str, step_details: "RunStepDeltaToolCallObject"
+) -> StreamingChatMessageContent | None:
+    """Generate streaming content related to a Deep Research Tool.
+
+    Emits FunctionCallContent for the input and FunctionResultContent for the output
+    as they appear in streamed tool call deltas.
+    """
+    if not step_details.tool_calls:
+        return None
+
+    items: list[FunctionCallContent | FunctionResultContent] = []
+
+    for index, tool in enumerate(step_details.tool_calls):
+        if tool.type == "deep_research":
+            deep_research_dict: dict = tool.get("deep_research", {})
+            arguments = {"input": deep_research_dict.get("input")}
+            if any(v is not None for v in arguments.values()):
+                items.append(
+                    FunctionCallContent(
+                        id=tool.get("id"),
+                        index=index,
+                        name=tool.type,
+                        function_name=tool.type,
+                        arguments=arguments,
+                    )
+                )
+            result = deep_research_dict.get("output")
+            if result is not None:
+                items.append(
+                    FunctionResultContent(
+                        function_name=tool.type,
+                        id=tool.get("id"),
+                        result=result,
+                    )
+                )
+
+    return (
+        StreamingChatMessageContent(
+            role=AuthorRole.ASSISTANT,
+            name=agent_name,
+            choice_index=0,
+            items=items,  # type: ignore
+        )
+        if items
+        else None
+    )
+
+
 @experimental
 def generate_streaming_file_search_content(
     agent_name: str, step_details: "RunStepDeltaToolCallObject"
diff --git a/python/semantic_kernel/agents/azure_ai/agent_thread_actions.py b/python/semantic_kernel/agents/azure_ai/agent_thread_actions.py
index ecbbbc882063..62fb798bb11b 100644
--- a/python/semantic_kernel/agents/azure_ai/agent_thread_actions.py
+++ b/python/semantic_kernel/agents/azure_ai/agent_thread_actions.py
@@ -19,6 +19,7 @@
     RunStepBingCustomSearchToolCall,
     RunStepBingGroundingToolCall,
     RunStepCodeInterpreterToolCall,
+    RunStepDeepResearchToolCall,
     RunStepDeltaChunk,
     RunStepDeltaToolCallObject,
     RunStepFileSearchToolCall,
@@ -42,6 +43,7 @@
     generate_azure_ai_search_content,
     generate_bing_grounding_content,
     generate_code_interpreter_content,
+    generate_deep_research_content,
     generate_file_search_content,
     generate_function_call_content,
     generate_function_call_streaming_content,
@@ -53,6 +55,7 @@
     generate_streaming_azure_ai_search_content,
     generate_streaming_bing_grounding_content,
     generate_streaming_code_interpreter_content,
+    generate_streaming_deep_research_content,
     generate_streaming_file_search_content,
     generate_streaming_mcp_call_content,
     generate_streaming_mcp_content,
@@ -397,6 +400,18 @@ def sort_key(step: RunStep):
                                         agent_name=agent.name,
                                         mcp_tool_call=mcp_tool_call,
                                     )
+                                case AgentsNamedToolChoiceType.DEEP_RESEARCH:
+                                    logger.debug(
+                                        f"Entering tool_calls (deep_research) for run [{run.id}], agent "
+                                        f" `{agent.name}` and thread `{thread_id}`"
+                                    )
+                                    deep_research_call: RunStepDeepResearchToolCall = cast(
+                                        RunStepDeepResearchToolCall, tool_call
+                                    )
+                                    content = generate_deep_research_content(
+                                        agent_name=agent.name,
+                                        deep_research_tool_call=deep_research_call,
+                                    )
 
                             if content:
                                 message_count += 1
@@ -629,6 +644,10 @@ async def _process_stream_events(
                                     content = generate_streaming_mcp_content(
                                         agent_name=agent.name, step_details=details
                                     )
+                                case AgentsNamedToolChoiceType.DEEP_RESEARCH:
+                                    content = generate_streaming_deep_research_content(
+                                        agent_name=agent.name, step_details=details
+                                    )
                             if content:
                                 if thread_msg_id and THREAD_MESSAGE_ID not in content.metadata:
                                     content.metadata[THREAD_MESSAGE_ID] = thread_msg_id
@@ -886,8 +905,8 @@ def _merge_options(
         return {
             "model": model if model is not None else agent.definition.model,
             "response_format": response_format if response_format is not None else agent.definition.response_format,
-            "temperature": temperature if temperature is not None else agent.definition.temperature,
-            "top_p": top_p if top_p is not None else agent.definition.top_p,
+            "temperature": temperature if temperature is not None else None,
+            "top_p": top_p if top_p is not None else None,
             "metadata": metadata if metadata is not None else agent.definition.metadata,
             **kwargs,
         }
diff --git a/python/semantic_kernel/agents/azure_ai/azure_ai_agent_settings.py b/python/semantic_kernel/agents/azure_ai/azure_ai_agent_settings.py
index 225126c8f59b..a3c3e4493778 100644
--- a/python/semantic_kernel/agents/azure_ai/azure_ai_agent_settings.py
+++ b/python/semantic_kernel/agents/azure_ai/azure_ai_agent_settings.py
@@ -25,3 +25,4 @@ class AzureAIAgentSettings(KernelBaseSettings):
     azure_ai_search_connection_id: str | None = None
     azure_ai_search_index_name: str | None = None
     api_version: str | None = None
+    deep_research_model: str | None = None
diff --git a/python/uv.lock b/python/uv.lock
index 4cea2d546e08..d7e728e8f1c8 100644
--- a/python/uv.lock
+++ b/python/uv.lock
@@ -1,5 +1,4 @@
 version = 1
-revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '4' and sys_platform == 'darwin'",
@@ -37,18 +36,18 @@ dependencies = [
     { name = "safetensors", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
     { name = "torch", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f7/66/be171836d86dc5b8698b3a9bf4b9eb10cb53369729939f88bf650167588b/accelerate-1.10.0.tar.gz", hash = "sha256:8270568fda9036b5cccdc09703fef47872abccd56eb5f6d53b54ea5fb7581496", size = 392261, upload-time = "2025-08-07T10:54:51.664Z" }
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