code: def beam_search(
      model,
      inputs=None,
      batch_size=1,
      n_beams=2,
      start_id=0,
      eos_id=1,
      max_length=100,
      length_penalty=1.0,
      accelerate=True,
  ):
      """Returns a batch of n_beams-sequences created by beam search.
  
      This function uses `model` to generate outputs one position at a time, with
      access to inputs for the current position and all preceding positions. The
      new output becomes the next position's input, and this loop repeats until
      either the model outputs the `eos_id` value or the output sequence reaches
      `max_length` items -- but keeping n_beams top beams.
  
      Args:
        model: A layer object (subclass of `trax.layers.Layer`) created in
            `'predict'` mode and initialized from trained weights. The model
            must have a structure that allows it to run as autoregressive
            one-sample-at-a-time predictor (e.g., `trax.models.TransformerLM`).
        inputs: Sequence of symbols the model sees as input the first time it
            generates an output. If None, the model must generate the first output
            with no input to guide it.
        batch_size: Number of sequences to generate in parallel as a batch.
        n_beams: How many beams to consider at the same time.
        start_id: The start symbol (ID/integer) for the autoregressive process,
            or array of shape (`batch_size`, 1) of such integers.
        eos_id: The end-of-sequence symbol (ID/integer) for the autoregressive
            process.
        max_length: Maximum length for generated sequences.
        length_penalty: Factor alpha in calculating the length penalty for beams.
        accelerate: If True, create an accelerated version of `model` and use it
            for generating outputs.
  
      Returns:
        Tensor of integers with shape (`batch_size`, n_beams, output_length) with
        a batch of output sequences. output_length is the maximum length of the
        output sequences, where each sequence can be no longer than `max_length`.
      """
      del (
          eos_id,
          length_penalty,
      )  # TODO(lukaszkaiser): add length penalty, eos
      assert batch_size == 1, "Batch size > 1 not supported yet"
      if inputs is not None and inputs.shape[0] != batch_size:
          raise ValueError(
              f"Inputs batch size ({inputs.shape[0]}) does not match "
              f"batch_size arg ({batch_size}."
          )
  
      fast_model = tl.Accelerate(model) if accelerate else model
      if np.isscalar(start_id):
          start_symbol = np.full(
              (batch_size, 1), start_id, dtype=np.int32
          )
      else:
          start_symbol = start_id
      if model.n_in == 1 and inputs is not None:
          current_symbols = np.concatenate(
              [start_symbol, inputs], axis=1
          )
      else:
          current_symbols = start_symbol
  
      beams = [current_symbols for _ in range(n_beams)]
      results = [([], 0.0) for _ in range(n_beams)]
      states = [fast_model.state for _ in range(n_beams)]
      top_k = [None] * n_beams
      counter = 0
      while counter < max_length:
          counter += 1
          # Run the model on all beams, collect states and top_k for each beam.
          for beam_id in range(n_beams if counter > 1 else 1):
              fast_model.state = states[beam_id]
              if model.n_in > 1 and inputs is not None:
                  logits = fast_model((inputs, beams[beam_id]))[0]
              else:
                  logits = fast_model(beams[beam_id])
              logits = tl.log_softmax(logits[:, -1, :])
              states[beam_id] = fast_model.state
              top_k[beam_id] = fastmath.top_k(logits, k=n_beams)
  
          # Select new beams.
          cur_values = (
              []
          )  # will hold triples (sum-of-logprobs, beam-id, symbol)
          for beam_id in range(n_beams if counter > 1 else 1):
              for k in range(n_beams):
                  values, symbols = top_k[beam_id]
                  value, symbol = values[:, k], symbols[:, k]
                  cur_values.append(
                      (results[beam_id][1] + value, beam_id, symbol)
                  )
          cur_values.sort(
              key=lambda x: -x[0][0]
          )  # x[0][0] as batch_size=1
          # Collect top beams to the new states and results.
          new_results, new_states, new_beams = [], [], []
          for value, beam_id, symbol in cur_values[:n_beams]:
              new_results.append(
                  (results[beam_id][0] + [symbol], value)
              )
              new_states.append(states[beam_id])  # copy?
              new_beams.append(symbol[:, None])
          results, states, beams = new_results, new_states, new_beams
  
      return [(np.stack(r, axis=-1), v) for (r, v) in results]
  
imports: import numpy
  from trax import fastmath
  from trax import layers
label_desc: Use image node_id 3 for calling a global function with example usage: beam_search(model, inputs, batch_size, n_beams, start_id, eos_id, max_length, length_penalty, accelerate) and returns: unknown