code: def flatten_state(state, num_wires):
      """
      Given a non-flat, potentially batched state, flatten it.
  
      Args:
          state (TensorLike): A state that needs flattening
          num_wires (int): The number of wires the state represents
  
      Returns:
          A flat state, with an extra batch dimension if necessary
      """
      dim = 2**num_wires
      batch_size = math.get_batch_size(state, (2,) * num_wires, dim)
      shape = (batch_size, dim) if batch_size is not None else (dim,)
      return math.reshape(state, shape)
  
file_path: pennylane/pennylane/devices/qubit/measure.py
imports: from typing import Callable
  from scipy.sparse import csr_matrix
  from pennylane import math
  from pennylane.ops import Sum, Hamiltonian
  from pennylane.measurements import StateMeasurement, MeasurementProcess, MeasurementValue, ExpectationMP
  from pennylane.pauli.conversion import is_pauli_sentence, pauli_sentence
  from pennylane.typing import TensorLike
  from pennylane.wires import Wires
  from .apply_operation import apply_operation
label_desc: Use image node_id 1 for calling a global function with example usage: flatten_state(state, num_wires) and returns: math