class_docstr: A value function using a MLP network.
  
  It fits the input data by performing linear regression
  to the outputs.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      num_seq_inputs (float): Number of sequence per input. By default
          it is 1.0, which means only one single sequence.
      name (str): Name of baseline.
      hidden_sizes (list[int]): Output dimension of dense layer(s) for
          the MLP for mean. For example, (32, 32) means the MLP consists
          of two hidden layers, each with 32 hidden units.
      hidden_nonlinearity (Callable): Activation function for intermediate
          dense layer(s). It should return a tf.Tensor. Set it to
          None to maintain a linear activation.
      hidden_w_init (Callable): Initializer function for the weight
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      hidden_b_init (Callable): Initializer function for the bias
          of intermediate dense layer(s). The function should return a
          tf.Tensor.
      output_nonlinearity (Callable): Activation function for output dense
          layer. It should return a tf.Tensor. Set it to None to
          maintain a linear activation.
      output_w_init (Callable): Initializer function for the weight
          of output dense layer(s). The function should return a
          tf.Tensor.
      output_b_init (Callable): Initializer function for the bias
          of output dense layer(s). The function should return a
          tf.Tensor.
      optimizer (garage.tf.Optimizer): Optimizer for minimizing the negative
          log-likelihood.
      optimizer_args (dict): Arguments for the optimizer. Default is None,
          which means no arguments.
      normalize_inputs (bool): Bool for normalizing inputs or not.
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docstr: Predict value based on paths.
  
  Args:
      paths (dict[numpy.ndarray]): Sample paths.
  
  Returns:
      numpy.ndarray: Predicted value.
docstr_tok: ["Predict","value","based","on","paths",".","Args",":","paths","(","dict","[","numpy.ndarray","]",")",":","Sample","paths",".","Returns",":","numpy.ndarray",":","Predicted","value","."]
code: def predict(self, paths):
      """Predict value based on paths.
  
      Args:
          paths (dict[numpy.ndarray]): Sample paths.
  
      Returns:
          numpy.ndarray: Predicted value.
  
      """
      obs = paths["observations"]
      if not isinstance(obs, np.ndarray) or len(obs.shape) > 2:
          obs = self._env_spec.observation_space.flatten_n(obs)
      return self._f_predict(obs).flatten()
  
file_path: garage/src/garage/tf/baselines/continuous_mlp_baseline.py
imports: from dowel import tabular
  import numpy
  import tensorflow
  from garage import make_optimizer
  from garage.experiment import deterministic
  from garage.np.baselines import Baseline
  from garage.tf import compile_function
  from garage.tf.models import NormalizedInputMLPModel
  from garage.tf.optimizers import LBFGSOptimizer
label_desc: Use image node_id 4 for calling the ContinuousMLPBaseline obj's underlying member method code with example usage: obj.predict(paths) and returns: self