code: def linear(
      input_tensor, scope, n_hidden, *, init_scale=1.0, init_bias=0.0
  ):
      """
      Creates a fully connected layer for TensorFlow
  
      :param input_tensor: (TensorFlow Tensor) The input tensor for the fully connected layer
      :param scope: (str) The TensorFlow variable scope
      :param n_hidden: (int) The number of hidden neurons
      :param init_scale: (int) The initialization scale
      :param init_bias: (int) The initialization offset bias
      :return: (TensorFlow Tensor) fully connected layer
      """
      with tf.variable_scope(scope):
          n_input = input_tensor.get_shape()[1].value
          weight = tf.get_variable(
              "w",
              [n_input, n_hidden],
              initializer=ortho_init(init_scale),
          )
          bias = tf.get_variable(
              "b",
              [n_hidden],
              initializer=tf.constant_initializer(init_bias),
          )
          return tf.matmul(input_tensor, weight) + bias
  
file_path: stable-baselines/stable_baselines/common/tf_layers.py
imports: import numpy
  import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: linear(input_tensor, scope, n_hidden) and returns: unknown