aws-sdk-go-v2/private/protocol"
)

// Please also see https://docs.aws.amazon.com/goto/WebAPI/mediaconnect-2018-11-14/StopFlowRequest
type StopFlowInput struct {
	_ struct{} `type:"structure"`

	// FlowArn is a required field
	FlowArn *string `location:"uri" locationName:"flowArn" type:"string" required:"true"`
}

// String returns the string representation
func (s StopFlowInput) String() string {
	return awsutil.Prettify(s)
}

// Validate inspects the fields of the type to determine if they are valid.
func (s *StopFlowInput) Validate() error {
	invalidParams := aws.ErrInvalidParams{Context: "StopFlowInput"}

	if s.FlowArn == nil {
		invalidParams.Add(aws.NewErrParamRequired("FlowArn"))
	}

	if invalidParams.Len() > 0 {
		return invalidParams
	}
	return nil
}

// MarshalFields encodes the AWS API shape using the passed in protocol encoder.
func (s StopFlowInput) MarshalFields(e protocol.FieldEncoder) error {
	e.SetValue(protocol.HeaderTarget, "Content-Type", protocol.StringValue("application/x-amz-json-1.1"), protocol.Metadata{})

	if s.FlowArn!= nil {
		v := *s.FlowArn

		metadata := protocol.Metadata{}
		e.SetValue(protocol.PathTarget, "flowArn", protocol.QuotedValue{ValueMarshaler: protocol.StringValue(v)}, metadata)
	}
	return nil
}

// The result of a successful StopFlow request.
// Please also see https://docs.aws.amazon.com/goto/WebAPI/mediaconnect-2018-11-14/StopFlowResponse
type StopFlowOutput struct {
	_ struct{} `type:"structure"`

	// The ARN of the flow that you stopped.
	FlowArn *string `locationName:"flowArn" type:"string"`

	// The status of the flow when the StopFlow process begins.
	Status Status `locationName:"status" type:"string" enum:"true"`
}

// String returns the string representation
func (s StopFlowOutput) String() string {
	return awsutil.Prettify(s)
}

// MarshalFields encodes the AWS API shape using the passed in protocol encoder.
func (s StopFlowOutput) MarshalFields(e protocol.FieldEncoder) error {
	if s.FlowArn!= nil {
		v := *s.FlowArn

		metadata := protocol.Metadata{}
		e.SetValue(protocol.BodyTarget, "flowArn", protocol.QuotedValue{ValueMarshaler: protocol.StringValue(v)}, metadata)
	}
	if len(s.Status) > 0 {
		v := s.Status

		metadata := protocol.Metadata{}
		e.SetValue(protocol.BodyTarget, "status", protocol.QuotedValue{ValueMarshaler: v}, metadata)
	}
	return nil
}

const opStopFlow = "StopFlow"

// StopFlowRequest returns a request value for making API operation for
// AWS MediaConnect.
//
// Stops a flow.
//
//    // Example sending a request using StopFlowRequest.
//    req := client.StopFlowRequest(params)
//    resp, err := req.Send(context.TODO())
//    if err == nil {
//        fmt.Println(resp)
//    }
//
// Please also see https://docs.aws.amazon.com/goto/WebAPI/mediaconnect-2018-11-14/StopFlow
func (c *Client) StopFlowRequest(input *StopFlowInput) StopFlowRequest {
	op := &aws.Operation{
		Name:       opStopFlow,
		HTTPMethod: "POST",
		HTTPPath:   "/v1/flows/stop/{flowArn}",
	}

	if input == nil {
		input = &StopFlowInput{}
	}

	req := c.newRequest(op, input, &StopFlowOutput{})
	return StopFlowRequest{Request: req, Input: input, Copy: c.StopFlowRequest}
}

// StopFlowRequest is the request type for the
// StopFlow API operation.
type StopFlowRequest struct {
	*aws.Request
	Input *StopFlowInput
	Copy  func(*StopFlowInput) StopFlowRequest
}

// Send marshals and sends the StopFlow API request.
func (r StopFlowRequest) Send(ctx context.Context) (*StopFlowResponse, error) {
	r.Request.SetContext(ctx)
	err := r.Request.Send()
	if err!= nil {
		return nil, err
	}

	resp := &StopFlowResponse{
		StopFlowOutput: r.Request.Data.(*StopFlowOutput),
		response:       &aws.Response{Request: r.Request},
	}

	return resp, nil
}

// StopFlowResponse is the response type for the
// StopFlow API operation.
type StopFlowResponse struct {
	*StopFlowOutput

	response *aws.Response
}

// SDKResponseMetdata returns the response metadata for the
// StopFlow request.
func (r *StopFlowResponse) SDKResponseMetdata() *aws.Response {
	return r.response
}
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In this task, find the most appropriate number to replace the blank (indicated with _ ) and express it in words.

Metabolism consists of _ types of processes catabolism and anabolism.
Output:
twoA dance performance showcasing pupils from KS3 & KS4 using a wide range of dance styles.
Happiness Is… explores the meaning of happiness within different communities and how dance as an art form allows people to express their emotions.
Happiness can be found even in the darkest of times, but only for those who remember to turn on the light!the paper uses GLM to model high-frequency data, $D_i, N_i, \Delta t_i$, and $F_{i-1}$.
Here $D_i$ follows a 0-1 distribution, definitely a distribution of exponential family. Intuitively the link function $g^{-1}(X^T \beta)$ is the CDF of normal distribution, the probit link function.
The GLM need $X^t\beta$ in the link function to be a linear form, but ${- \mu_i \over \sigma_i}$ is not. Should I choose another link function or should I reform the $ {- \mu_i \over \sigma_i}$ (since the model proposed $ln(\sigma_i)$ form)?
For the computational reason, I mapped $D_i$ from -1,1 to 1,0. Do I need to map regressors as well? I guess not. Cause if I do, i. the calculation of $ \vert \sum_{j=1}^4 D_{i-j} \vert$will be different. ii. I am just using a different tag for the same 'truth'.
I have spent days searching and scratching this problem. I presume it could be a quite common modeling procedure since people normally want to model expectation and variance. It will be very appreciated if you may kindly provide me some guide.
What you have seems to be a binomial regression model with probit link function, but in place of the usual linear predictor $\beta^T x$ you have some nonlinear predictor $f(\beta, x)$.
Binomial regression is usually fitted by irls algorithm, see Can you give a simple intuitive explanation of IRLS method to find the MLE of a GLM?. It should be possible to replace the linear least squares used there with nonlinear least squares, but I do not know of implementations. But looking closer at your model, you have a linear predictor for $\mu_i$, but then you also have a linear predictor (with a log link function) for $\sigma_i$. So you are doing simultaneous modelling of two parameters, so you need an extension of the usual glm framework. I would have a look at the gamlss package for R, see http://www.gamlss.