User: Write the solution for
Please complete the compute_gradient function to compute  ∂𝐽(𝐰,𝑏)∂𝑤
 ,  ∂𝐽(𝐰,𝑏)∂𝑏
  from equations (2) and (3) below.

∂𝐽(𝐰,𝑏)∂𝑏=1𝑚∑𝑖=0𝑚−1(𝑓𝐰,𝑏(𝐱(𝑖))−𝐲(𝑖))(2)
∂𝐽(𝐰,𝑏)∂𝑤𝑗=1𝑚∑𝑖=0𝑚−1(𝑓𝐰,𝑏(𝐱(𝑖))−𝐲(𝑖))𝑥(𝑖)𝑗(3)

m is the number of training examples in the dataset
𝑓𝐰,𝑏(𝑥(𝑖))
  is the model's prediction, while  𝑦(𝑖)
  is the actual label
Note: While this gradient looks identical to the linear regression gradient, the formula is actually different because linear and logistic regression have different definitions of  𝑓𝐰,𝑏(𝑥)
 .
As before, you can use the sigmoid function that you implemented above and if you get stuck, you can check out the hints presented after the cell below to help you with the implementation.
Here is the starter
def compute_gradient(X, y, w, b, *argv): 
    """
    Computes the gradient for logistic regression 
 
    Args:
      X : (ndarray Shape (m,n)) data, m examples by n features
      y : (ndarray Shape (m,))  target value 
      w : (ndarray Shape (n,))  values of parameters of the model      
      b : (scalar)              value of bias parameter of the model
      *argv : unused, for compatibility with regularized version below
    Returns
      dj_dw : (ndarray Shape (n,)) The gradient of the cost w.r.t. the parameters w. 
      dj_db : (scalar)             The gradient of the cost w.r.t. the parameter b. 
    """
    m, n = X.shape
    dj_dw = np.zeros(w.shape)
    dj_db = 0.

    ### START CODE HERE ### 
    for i in range(m):
        z_wb = None
        for j in range(n): 
            z_wb += None
        z_wb += None
        f_wb = None
        
        dj_db_i = None
        dj_db += None
        
        for j in range(n):
            dj_dw[j] = None
            
    dj_dw = None
    dj_db = None
    ### END CODE HERE ###

        
    return dj_db, dj_dw
Assistant: Sure! Here’s how you can complete the `compute_gradient` function to compute the gradients for logistic regression based on the equations provided:

```python
import numpy as np

def compute_gradient(X, y, w, b, *argv):
    """ Computes the gradient for logistic regression
    Args:
        X : (ndarray Shape (m,n)) data, m examples by n features
        y : (ndarray Shape (m,)) target value
        w : (ndarray Shape (n,)) values of parameters of the model
        b : (scalar) value of bias parameter of the model
        *argv : unused, for compatibility with regularized version below
    Returns:
        dj_dw : (ndarray Shape (n,)) The gradient of the cost w.r.t. the parameters w.
        dj_db : (scalar) The gradient of the cost w.r.t. the parameter b.
    """
    m, n = X.shape
    dj_dw = np.zeros(w.shape)
    dj_db = 0.0

    ### START CODE HERE ###
    for i in range(m):
        # Compute the linear combination of inputs and weights
        z_wb = np.dot(X[i], w) + b
        
        # Apply the sigmoid function to get the prediction
        f_wb = 1 / (1 + np.exp(-z_wb))
        
        # Compute the error
        error = f_wb - y[i]
        
        # Update the bias gradient
        dj_db += error
        
        # Update the weights gradient
        for j in range(n):
            dj_dw[j] += error * X[i][j]
    
    # Average the gradients over all examples
    dj_db /= m
    dj_dw /= m
    ### END CODE HERE ###

    return dj_db, dj_dw
```

### Explanation:
1. **Initialization**: We initialize `dj_dw` as a zero vector of the same shape as `w` and `dj_db` as zero.
2. **Loop through each training example**: For each example, we compute the linear combination of inputs and weights (`z_wb`), apply the sigmoid function to get the prediction (`f_wb`), and then calculate the error.
3. **Gradient Calculation**: We update `dj_db` with the error and accumulate the gradients for each weight in `dj_dw`.
4. **Averaging**
User: make the solution match the starter
Assistant: