class_docstr: Svdf implementations with conv layers.
  
  This is an equalivent implementation to class SvdfCell in svdf_cell.py.
  The difference is, training with SvdfConvLayer implementation is much faster,
  but SvdfCell is better during inference time as it is implemented in streaming
  way.
  
  # Attributes:
    units: Positive integer, dimensionality of the output space.
    memory_size: Dimension of feature vector.
    rank: int or long, the rank of the SVD approximation.
    activation: Activation function to use
      (see [activations](../activations.md)).
      Default: Rectified linear unit (`relu`).
      If you pass `None`, no activation is applied
      (ie. "linear" activation: `a(x) = x`).
    use_bias: Boolean, whether the layer uses a bias vector.
    kernel_initializer: Initializer for the `kernel` weights matrix,
      used for the linear transformation of the inputs
      (see [initializers](../initializers.md)).
    bias_initializer: Initializer for the bias vector
      (see [initializers](../initializers.md)).
    dropout: Float between 0 and 1.
      Fraction of the units to drop for
      the linear transformation of the inputs.
    use_batch_norm: whether to apply batch normalization
    bn_scale: whether to apply batch norm scaling. When the next layer is linear
      (e.g. nn.relu), this can be disabled since the scaling will be done by the
      next layer.
docstr: Configs of the model hparams for logging and debugging purposes.
code: def get_config(self) -> Dict[Text, Any]:
      """Configs of the model hparams for logging and debugging purposes."""
      config = {
          "units": self.units,
          "memory_size": self.memory_size,
          "rank": self.rank,
          "activation": tf.keras.activations.serialize(self.activation),
          "use_bias": self.use_bias,
          "kernel_initializer": tf.keras.initializers.serialize(
              self.kernel_initializer
          ),
          "bias_initializer": tf.keras.initializers.serialize(
              self.bias_initializer
          ),
          "dropout": self.dropout,
      }
      base_config = super(SvdfConvLayer, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
file_path: model_search/model_search/ops/svdf_conv.py
imports: from typing import Any, Callable, Dict, List, Optional, Text, Union
  from absl import logging
  from six.moves import range
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling the SvdfConvLayer obj's underlying member method code with example usage: obj.get_config() and returns: dict