class_docstr: Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
  Args:
    args: a 2D Tensor or a list of 2D, batch x n, Tensors.
    output_size: int, second dimension of weight variable.
    dtype: data type for variables.
    build_bias: boolean, whether to build a bias variable.
    bias_initializer: starting value to initialize the bias
      (default is all zeros).
    kernel_initializer: starting value to initialize the weight.
  Raises:
    ValueError: if inputs_shape is wrong.
code: def __init__(
      self,
      args,
      output_size,
      build_bias,
      bias_initializer=None,
      kernel_initializer=None,
  ):
      self._build_bias = build_bias
  
      if args is None or (nest.is_sequence(args) and not args):
          invalidInputError(False, "`args` must be specified")
      if not nest.is_sequence(args):
          args = [args]
          self._is_sequence = False
      else:
          self._is_sequence = True
  
      # Calculate the total size of arguments on dimension 1.
      total_arg_size = 0
      shapes = [a.get_shape() for a in args]
      for shape in shapes:
          if shape.ndims != 2:
              invalidInputError(
                  False, "linear is expecting 2D arguments: %s" % shapes
              )
          if tf.__version__[0] == "1":
              if shape[1].value is None:
                  invalidInputError(
                      False,
                      "linear expects shape[1] to be provided for shape %s, "
                      "but saw %s" % (shape, shape[1]),
                  )
              else:
                  total_arg_size += shape[1].value
          elif tf.__version__[0] == "2":
              if shape[1] is None:
                  invalidInputError(
                      False,
                      "linear expects shape[1] to be provided for shape %s, "
                      "but saw %s" % (shape, shape[1]),
                  )
              else:
                  total_arg_size += shape[1]
  
      dtype = [a.dtype for a in args][0]
  
      scope = vs.get_variable_scope()
      with vs.variable_scope(scope) as outer_scope:
          self._weights = vs.get_variable(
              _WEIGHTS_VARIABLE_NAME,
              [total_arg_size, output_size],
              dtype=dtype,
              initializer=kernel_initializer,
          )
          if build_bias:
              with vs.variable_scope(outer_scope) as inner_scope:
                  inner_scope.set_partitioner(None)
                  if bias_initializer is None:
                      bias_initializer = init_ops.constant_initializer(
                          0.0, dtype=dtype
                      )
                  self._biases = vs.get_variable(
                      _BIAS_VARIABLE_NAME,
                      [output_size],
                      dtype=dtype,
                      initializer=bias_initializer,
                  )
  
imports: import tensorflow
  from tensorflow.python.ops.rnn_cell import *
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.util import nest
  from tensorflow.python.ops import nn_ops
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 1 to create a new _Linear object from inherited base classes: object with example: obj = _Linear(args, output_size, build_bias, bias_initializer, kernel_initializer)