def call(self, inputs):
    inputs = ops.convert_to_tensor(inputs, dtype=self.dtype)
    shape = inputs.get_shape().as_list()
    output_shape = shape[:-1] + [self.units]
    if len(output_shape) > 2:
      # Broadcasting is required for the inputs.
      outputs = standard_ops.tensordot(inputs, self.kernel, [[len(shape) - 1],
                                                             [0]])
      # Reshape the output back to the original ndim of the input.
      outputs.set_shape(output_shape)
    else:
      outputs = standard_ops.matmul(inputs, self.kernel)
    if self.use_bias:
      outputs = nn.bias_add(outputs, self.bias)
    if self.activation is not None:
      return self.activation(outputs)  # pylint: disable=not-callable
    return outputs 

def call(self, inputs):
    shape = inputs.get_shape().as_list()
    input_dim = shape[-1]
    output_shape = shape[:-1] + [self.units]
    if len(output_shape) > 2:
      # Reshape the input to 2D.
      output_shape_tensors = array_ops.unstack(array_ops.shape(inputs))
      output_shape_tensors[-1] = self.units
      output_shape_tensor = array_ops.stack(output_shape_tensors)
      inputs = array_ops.reshape(inputs, [-1, input_dim])

    outputs = standard_ops.matmul(inputs, self.kernel)
    if self.use_bias:
      outputs = nn.bias_add(outputs, self.bias)

    if len(output_shape) > 2:
      # Reshape the output back to the original ndim of the input.
      outputs = array_ops.reshape(outputs, output_shape_tensor)
      outputs.set_shape(output_shape)

    if self.activation is not None:
      return self.activation(outputs)  # pylint: disable=not-callable
    return outputs 

def call(self, inputs):
    inputs = ops.convert_to_tensor(inputs, dtype=self.dtype)
    shape = inputs.get_shape().as_list()
    if len(shape) > 2:
      # Broadcasting is required for the inputs.
      outputs = standard_ops.tensordot(inputs, self.kernel, [[len(shape) - 1],
                                                             [0]])
      # Reshape the output back to the original ndim of the input.
      if context.in_graph_mode():
        output_shape = shape[:-1] + [self.units]
        outputs.set_shape(output_shape)
    else:
      outputs = standard_ops.matmul(inputs, self.kernel)
    if self.use_bias:
      outputs = nn.bias_add(outputs, self.bias)
    if self.activation is not None:
      return self.activation(outputs)  # pylint: disable=not-callable
    return outputs 

def call(self, inputs):
    shape = inputs.get_shape().as_list()
    input_dim = shape[-1]
    output_shape = shape[:-1] + [self.units]
    if len(output_shape) > 2:
      # Reshape the input to 2D.
      output_shape_tensors = array_ops.unstack(array_ops.shape(inputs))
      output_shape_tensors[-1] = self.units
      output_shape_tensor = array_ops.stack(output_shape_tensors)
      inputs = array_ops.reshape(inputs, [-1, input_dim])

    outputs = standard_ops.matmul(inputs, self.kernel)
    if self.use_bias:
      outputs = nn.bias_add(outputs, self.bias)

    if len(output_shape) > 2:
      # Reshape the output back to the original ndim of the input.
      outputs = array_ops.reshape(outputs, output_shape_tensor)
      outputs.set_shape(output_shape)

    if self.activation is not None:
      return self.activation(outputs)  # pylint: disable=not-callable
    return outputs