code: def fit(self, X, y):
      n_samples, n_features = X.shape
      self.w = np.zeros(n_features, dtype=np.float64)
      self.b = 0.0
  
      for t in range(self.n_iter):
          for i in range(n_samples):
              p = self.project(X[i])
              if self.loss in ("hinge", "squared_hinge"):
                  loss = max(1 - y[i] * p, 0)
              else:
                  loss = max(np.abs(p - y[i]) - self.epsilon, 0)
  
              sqnorm = np.dot(X[i], X[i])
  
              if self.loss in ("hinge", "epsilon_insensitive"):
                  step = min(self.C, loss / sqnorm)
              elif self.loss in (
                  "squared_hinge",
                  "squared_epsilon_insensitive",
              ):
                  step = loss / (sqnorm + 1.0 / (2 * self.C))
  
              if self.loss in ("hinge", "squared_hinge"):
                  step *= y[i]
              else:
                  step *= np.sign(y[i] - p)
  
              self.w += step * X[i]
              if self.fit_intercept:
                  self.b += step
  
imports: import numpy
  import pytest
  from sklearn.base import ClassifierMixin
  from sklearn.datasets import load_iris
  from sklearn.linear_model import PassiveAggressiveClassifier, PassiveAggressiveRegressor
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 2 for calling the MyPassiveAggressive obj's underlying member method code with example usage: obj.fit(X, y) without return types