docstr: Predict count for samples in X.
  
  Parameters
  ----------
  X : array-like, shape = [n_samples, n_features]
  
  Returns
  -------
  C : array, shape = [n_samples,]
      Predicted count for each sample
docstr_tok: ["Predict","count","for","samples","in","X.","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","[","n_samples",",","n_features","]","Returns","--","--","--","-","C",":","array",",","shape","=","[","n_samples",",","]","Predicted","count","for","each","sample"]
code: def predict(self, X):
      """Predict count for samples in X.
  
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
  
      Returns
      -------
      C : array, shape = [n_samples,]
          Predicted count for each sample
      """
      X_ = self._check_array(X)
      return exp(dot(X_, self._coef))
  
imports: import textwrap
  from dask_glm import algorithms, families
  from dask_glm.utils import accuracy_score, add_intercept, dot, exp, poisson_deviance, sigmoid
  from sklearn.base import BaseEstimator
  from ..metrics import r2_score
  from ..utils import check_array
  from .utils import lr_prob_stack
label_desc: Use image node_id 2 for calling the PoissonRegression obj's underlying member method code with example usage: obj.predict(X) and returns: exp