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list_environments

list_environments(
    *,
    config_overrides: Optional[ProtonClientConfig] = None,
    next_token: Optional[NextToken] = None,
    max_results: Optional[MaxPageResults] = None,
    environment_templates: Optional[EnvironmentTemplateFilterList] = None,
) -> ListEnvironmentsOutput

List environments with detail data summaries.

Parameters:

  • next_token (Optional[NextToken], default: None ) –

    A token that indicates the location of the next environment in the array of environments, after the list of environments that was previously requested.

  • max_results (Optional[MaxPageResults], default: None ) –

    The maximum number of environments to list.

  • environment_templates (Optional[EnvironmentTemplateFilterList], default: None ) –

    An array of the versions of the environment template.

Raises:

Source code in docs_src/capo_proton/_services/proton.py
def list_environments(
    self,
    *,
    config_overrides: Optional[ProtonClientConfig] = None,
    next_token: Optional["capo_proton.types.next_token.NextToken"] = None,
    max_results: Optional[
        "capo_proton.types.max_page_results.MaxPageResults"
    ] = None,
    environment_templates: Optional[
        "capo_proton.types.environment_template_filter_list.EnvironmentTemplateFilterList"
    ] = None,
) -> "capo_proton.types.list_environments_output.ListEnvironmentsOutput":
    """<p>List environments with detail data summaries.</p>

    Args:
        next_token: <p>A token that indicates the location of the next environment in the array of environments, after the list of environments that was previously requested.</p>
        max_results: <p>The maximum number of environments to list.</p>
        environment_templates: <p>An array of the versions of the environment template.</p>

    Raises:
        capo_proton.errors.access_denied_exception.AccessDeniedException: <p>There <i>isn't</i> sufficient access for performing this action.</p>
        capo_proton.errors.internal_server_exception.InternalServerException: <p>The request failed to register with the service.</p>
        capo_proton.errors.resource_not_found_exception.ResourceNotFoundException: <p>The requested resource <i>wasn't</i> found.</p>
        capo_proton.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_proton.errors.validation_exception.ValidationException: <p>The input is invalid or an out-of-range value was supplied for the input parameter.</p>
        capo_proton.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_proton.types.list_environments_input.ListEnvironmentsInput]",
    ) -> OperationResponse[
        "capo_proton.types.list_environments_output.ListEnvironmentsOutput"
    ]:
        import capo_proton._operations.aws_proton20200720.list_environments

        output, http_response = (
            capo_proton._operations.aws_proton20200720.list_environments.list_environments(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_output(
        output: "capo_proton.types.list_environments_output.ListEnvironmentsOutput",
    ) -> dict[str, Any]:
        import capo_proton.types.list_environments_output

        return capo_proton.types.list_environments_output.redact_sensitive(output)

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("ListEnvironments", redact_output=_redact_output),
    )
    input_: capo_proton.types.list_environments_input.ListEnvironmentsInput = {}
    if next_token is not None:
        input_["next_token"] = next_token
    if max_results is not None:
        input_["max_results"] = max_results
    if environment_templates is not None:
        input_["environment_templates"] = environment_templates

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output