code: def compute_predicted_linear_response(
      model_matrix, model_coefficients, offset=None, name=None
  ):
      """Computes `model_matrix @ model_coefficients + offset`.
  
      Args:
        model_matrix: (Batch of) `float`-like, matrix-shaped `Tensor` where each row
          represents a sample's features.
        model_coefficients: (Batch of) vector-shaped `Tensor` representing the model
          coefficients, one for each column in `model_matrix`. Must have same
          `dtype` as `model_matrix`.
        offset: Optional `Tensor` representing constant shift applied to
          `predicted_linear_response`.  Must broadcast to `response`.
          Default value: `None` (i.e., `tf.zeros_like(predicted_linear_response)`).
        name: Python `str` used as name prefix to ops created by this function.
          Default value: `None` (i.e., `"compute_predicted_linear_response"`).
  
      Returns:
        predicted_linear_response: `response`-shaped `Tensor` representing linear
          predictions based on new `model_coefficients`, i.e.,
          `tf.linalg.matvec(model_matrix, model_coefficients) + offset`.
      """
      with tf.name_scope(name or "compute_predicted_linear_response"):
          if isinstance(
              model_matrix, (tf.SparseTensor, tf1.SparseTensorValue)
          ):
              matvecmul = sparse_or_dense_matvecmul
          else:
              matvecmul = tf.linalg.matvec
          predicted_linear_response = matvecmul(
              model_matrix, model_coefficients
          )
          if offset is not None:
              predicted_linear_response += offset
          return predicted_linear_response
  
file_path: probability/tensorflow_probability/python/glm/fisher_scoring.py
imports: import warnings
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.math.linalg import sparse_or_dense_matvecmul
label_desc: Use image node_id 5 for calling a global function with example usage: compute_predicted_linear_response(model_matrix, model_coefficients, offset, name) and returns: predicted_linear_response