class_docstr: Main function for Stochastic Adversarial Video Prediction.
docstr: Get expected fully connected shape after a series of convolutions.
docstr_tok: ["Get","expected","fully","connected","shape","after","a","series","of","convolutions","."]
code: def get_fc_dimensions(self, strides, kernel_sizes):
      """Get expected fully connected shape after a series of convolutions."""
      output_height, output_width, _ = self.hparams.problem.frame_shape
      output_steps = self.hparams.video_num_target_frames
      output_shape = np.array(
          [output_steps, output_height, output_width]
      )
      for curr_stride, kernel_size in zip(strides, kernel_sizes):
          output_shape = self.expected_output_shape(
              output_shape, np.array(curr_stride), 1, kernel_size
          )
      return (
          np.prod(output_shape)
          * self.hparams.num_discriminator_filters
          * 8
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numbers
  import numpy
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.models.video import savp_params
  from tensor2tensor.models.video import sv2p
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import update_ops_hook
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_gan
label_desc: Use image node_id 3 for calling the NextFrameSavpBase obj's underlying member method code with example usage: obj.get_fc_dimensions(strides, kernel_sizes) and returns: unknown