code: def create_model(
      batch_x,
      seq_length,
      dropout,
      reuse=False,
      batch_size=None,
      previous_state=None,
      overlap=True,
      rnn_impl=rnn_impl_lstmblockfusedcell,
  ):
      layers = {}
  
      # Input shape: [batch_size, n_steps, n_input + 2*n_input*n_context]
      if not batch_size:
          batch_size = tf.shape(input=batch_x)[0]
  
      # Create overlapping feature windows if needed
      if overlap:
          batch_x = create_overlapping_windows(batch_x)
  
      # Reshaping `batch_x` to a tensor with shape `[n_steps*batch_size, n_input + 2*n_input*n_context]`.
      # This is done to prepare the batch for input into the first layer which expects a tensor of rank `2`.
  
      # Permute n_steps and batch_size
      batch_x = tf.transpose(a=batch_x, perm=[1, 0, 2, 3])
      # Reshape to prepare input for first layer
      batch_x = tf.reshape(
          batch_x,
          [-1, Config.n_input + 2 * Config.n_input * Config.n_context],
      )  # (n_steps*batch_size, n_input + 2*n_input*n_context)
      layers["input_reshaped"] = batch_x
  
      # The next three blocks will pass `batch_x` through three hidden layers with
      # clipped RELU activation and dropout.
      layers["layer_1"] = layer_1 = dense(
          "layer_1",
          batch_x,
          Config.n_hidden_1,
          dropout_rate=dropout[0],
          layer_norm=FLAGS.layer_norm,
      )
      layers["layer_2"] = layer_2 = dense(
          "layer_2",
          layer_1,
          Config.n_hidden_2,
          dropout_rate=dropout[1],
          layer_norm=FLAGS.layer_norm,
      )
      layers["layer_3"] = layer_3 = dense(
          "layer_3",
          layer_2,
          Config.n_hidden_3,
          dropout_rate=dropout[2],
          layer_norm=FLAGS.layer_norm,
      )
  
      # `layer_3` is now reshaped into `[n_steps, batch_size, 2*n_cell_dim]`,
      # as the LSTM RNN expects its input to be of shape `[max_time, batch_size, input_size]`.
      layer_3 = tf.reshape(layer_3, [-1, batch_size, Config.n_hidden_3])
  
      # Run through parametrized RNN implementation, as we use different RNNs
      # for training and inference
      output, output_state = rnn_impl(
          layer_3, seq_length, previous_state, reuse
      )
  
      # Reshape output from a tensor of shape [n_steps, batch_size, n_cell_dim]
      # to a tensor of shape [n_steps*batch_size, n_cell_dim]
      output = tf.reshape(output, [-1, Config.n_cell_dim])
      layers["rnn_output"] = output
      layers["rnn_output_state"] = output_state
  
      # Now we feed `output` to the fifth hidden layer with clipped RELU activation
      layers["layer_5"] = layer_5 = dense(
          "layer_5",
          output,
          Config.n_hidden_5,
          dropout_rate=dropout[5],
          layer_norm=FLAGS.layer_norm,
      )
  
      # Now we apply a final linear layer creating `n_classes` dimensional vectors, the logits.
      layers["layer_6"] = layer_6 = dense(
          "layer_6", layer_5, Config.n_hidden_6, relu=False
      )
  
      # Finally we reshape layer_6 from a tensor of shape [n_steps*batch_size, n_hidden_6]
      # to the slightly more useful shape [n_steps, batch_size, n_hidden_6].
      # Note, that this differs from the input in that it is time-major.
      layer_6 = tf.reshape(
          layer_6,
          [-1, batch_size, Config.n_hidden_6],
          name="raw_logits",
      )
      layers["raw_logits"] = layer_6
  
      # Output shape: [n_steps, batch_size, n_hidden_6]
      return layer_6, layers
  
imports: from __future__ import absolute_import, division, print_function
  import os
  import sys
  import absl.app
  import numpy
  import progressbar
  import shutil
  import tensorflow
  import tensorflow.compat.v1
  import time
  from datetime import datetime
  from ds_ctcdecoder import ctc_beam_search_decoder, Scorer
  from .evaluate import evaluate
  from six.moves import zip, range
  from .util.config import Config, initialize_globals
  from .util.checkpoints import load_or_init_graph_for_training, load_graph_for_evaluation, reload_best_checkpoint
  from .util.evaluate_tools import save_samples_json
  from .util.feeding import create_dataset, audio_to_features, audiofile_to_features
  from .util.flags import create_flags, FLAGS
  from .util.helpers import check_ctcdecoder_version, ExceptionBox
  from .util.logging import create_progressbar, log_debug, log_error, log_info, log_progress, log_warn
  from .util.io import open_remote, remove_remote, listdir_remote, is_remote_path, isdir_remote
label_desc: Use image node_id 7 for calling a global function with example usage: create_model(batch_x, seq_length, dropout, reuse, batch_size, previous_state, overlap, rnn_impl) and returns: layer_6,  layers