Fix "test_tool_message_histories_list_content" integration tests for Mistral provider

[3coins]

The standard integration test "test_tool_message_histories_list_content" is failing for ChatBedrockConverse for Mistral models.

Error log

======================================================================= FAILURES =======================================================================
_________________________________________ TestBedrockMistralStandard.test_tool_message_histories_list_content __________________________________________

self = <tests.integration_tests.chat_models.test_bedrock_converse.TestBedrockMistralStandard object at 0x1055067a0>
model = ChatBedrockConverse(disable_streaming='tool_calling', client=<botocore.client.BedrockRuntime object at 0x105537cd0>, m...equences=[], temperature=0.0, region_name='us-west-2', provider='mistral', supports_tool_choice_values=('auto', 'any'))

    @pytest.mark.xfail(reason="Human messages following AI messages not supported.")
    def test_tool_message_histories_list_content(self, model: BaseChatModel) -> None:
>       super().test_tool_message_histories_list_content(model)

tests/integration_tests/chat_models/test_bedrock_converse.py:48: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/Users/pijain/Library/Caches/pypoetry/virtualenvs/langchain-aws-eH7P7gjZ-py3.10/lib/python3.10/site-packages/langchain_standard_tests/integration_tests/chat_models.py:431: in test_tool_message_histories_list_content
    result_list_content = model_with_tools.invoke(messages_list_content)
/Users/pijain/Library/Caches/pypoetry/virtualenvs/langchain-aws-eH7P7gjZ-py3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py:5343: in invoke
    return self.bound.invoke(
/Users/pijain/Library/Caches/pypoetry/virtualenvs/langchain-aws-eH7P7gjZ-py3.10/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:284: in invoke
    self.generate_prompt(
/Users/pijain/Library/Caches/pypoetry/virtualenvs/langchain-aws-eH7P7gjZ-py3.10/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:784: in generate_prompt
    return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
/Users/pijain/Library/Caches/pypoetry/virtualenvs/langchain-aws-eH7P7gjZ-py3.10/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:641: in generate
    raise e
/Users/pijain/Library/Caches/pypoetry/virtualenvs/langchain-aws-eH7P7gjZ-py3.10/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:631: in generate
    self._generate_with_cache(
/Users/pijain/Library/Caches/pypoetry/virtualenvs/langchain-aws-eH7P7gjZ-py3.10/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py:853: in _generate_with_cache
    result = self._generate(
langchain_aws/chat_models/bedrock_converse.py:492: in _generate
    response = self.client.converse(
/Users/pijain/Library/Caches/pypoetry/virtualenvs/langchain-aws-eH7P7gjZ-py3.10/lib/python3.10/site-packages/botocore/client.py:569: in _api_call
    return self._make_api_call(operation_name, kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <botocore.client.BedrockRuntime object at 0x105537cd0>, operation_name = 'Converse'
api_params = {'inferenceConfig': {'maxTokens': 100, 'stopSequences': [], 'temperature': 0.0}, 'messages': [{'content': [{'text': 'W...'success', 'toolUseId': 'abc123'}}], 'role': 'user'}], 'modelId': 'mistral.mistral-large-2402-v1:0', 'system': [], ...}

    def _make_api_call(self, operation_name, api_params):
        operation_model = self._service_model.operation_model(operation_name)
        service_name = self._service_model.service_name
        history_recorder.record(
            'API_CALL',
            {
                'service': service_name,
                'operation': operation_name,
                'params': api_params,
            },
        )
        if operation_model.deprecated:
            logger.debug(
                'Warning: %s.%s() is deprecated', service_name, operation_name
            )
        request_context = {
            'client_region': self.meta.region_name,
            'client_config': self.meta.config,
            'has_streaming_input': operation_model.has_streaming_input,
            'auth_type': operation_model.resolved_auth_type,
            'unsigned_payload': operation_model.unsigned_payload,
        }
    
        api_params = self._emit_api_params(
            api_params=api_params,
            operation_model=operation_model,
            context=request_context,
        )
        (
            endpoint_url,
            additional_headers,
            properties,
        ) = self._resolve_endpoint_ruleset(
            operation_model, api_params, request_context
        )
        if properties:
            # Pass arbitrary endpoint info with the Request
            # for use during construction.
            request_context['endpoint_properties'] = properties
        request_dict = self._convert_to_request_dict(
            api_params=api_params,
            operation_model=operation_model,
            endpoint_url=endpoint_url,
            context=request_context,
            headers=additional_headers,
        )
        resolve_checksum_context(request_dict, operation_model, api_params)
    
        service_id = self._service_model.service_id.hyphenize()
        handler, event_response = self.meta.events.emit_until_response(
            f'before-call.{service_id}.{operation_name}',
            model=operation_model,
            params=request_dict,
            request_signer=self._request_signer,
            context=request_context,
        )
    
        if event_response is not None:
            http, parsed_response = event_response
        else:
            maybe_compress_request(
                self.meta.config, request_dict, operation_model
            )
            apply_request_checksum(request_dict)
            http, parsed_response = self._make_request(
                operation_model, request_dict, request_context
            )
    
        self.meta.events.emit(
            f'after-call.{service_id}.{operation_name}',
            http_response=http,
            parsed=parsed_response,
            model=operation_model,
            context=request_context,
        )
    
        if http.status_code >= 300:
            error_info = parsed_response.get("Error", {})
            error_code = error_info.get("QueryErrorCode") or error_info.get(
                "Code"
            )
            error_class = self.exceptions.from_code(error_code)
>           raise error_class(parsed_response, operation_name)
E           botocore.errorfactory.ValidationException: An error occurred (ValidationException) when calling the Converse operation: messages.1.content: Conversation blocks and tool use blocks cannot be provided in the same turn.

/Users/pijain/Library/Caches/pypoetry/virtualenvs/langchain-aws-eH7P7gjZ-py3.10/lib/python3.10/site-packages/botocore/client.py:1023: ValidationException