def dense_kernel_initializer(shape, dtype=None, partition_info=None):
  """Initialization for dense kernels.

  This initialization is equal to
    tf.variance_scaling_initializer(scale=1.0/3.0, mode='fan_out',
                                    distribution='uniform').
  It is written out explicitly here for clarity.

  Args:
    shape: shape of variable
    dtype: dtype of variable
    partition_info: unused

  Returns:
    an initialization for the variable
  """
  del partition_info
  init_range = 1.0 / np.sqrt(shape[1])
  return tf.random_uniform(shape, -init_range, init_range, dtype=dtype)
Initialization for dense kernels.

  This initialization is equal to
    tf.variance_scaling_initializer(scale=1.0/3.0, mode='fan_out',
                                    distribution='uniform').
  It is written out explicitly here for clarity.

  Args:
    shape: shape of variable
    dtype: dtype of variable
    partition_info: unused

  Returns:
    an initialization for the variable
  
tf_to_pytorch/convert_tf_to_pt/original_tf/efficientnet_model.py