code: def dm_from_state_vector(
      state, check_state=False, c_dtype="complex128"
  ):
      """
      Convenience function to compute a (full) density matrix from
      a state vector.
  
      Args:
          state (tensor_like): 1D or 2D tensor state vector. This tensor should of size ``(2**N,)``
              or ``(batch_dim, 2**N)``, for some integer value ``N``.
          check_state (bool): If True, the function will check the state validity (shape and norm).
          c_dtype (str): Complex floating point precision type.
  
      Returns:
          tensor_like: Density matrix of size ``(2**N, 2**N)`` or ``(batch_dim, 2**N, 2**N)``
  
      **Example**
  
      >>> x = np.array([1, 0, 1j, 0]) / np.sqrt(2)
      >>> dm_from_state_vector(x)
      array([[0.5+0.j , 0. +0.j , 0. -0.5j, 0. +0.j ],
             [0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j ],
             [0. +0.5j, 0. +0.j , 0.5+0.j , 0. +0.j ],
             [0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j ]])
  
      """
      num_wires = int(np.log2(np.shape(state)[-1]))
      return reduce_statevector(
          state,
          indices=list(range(num_wires)),
          check_state=check_state,
          c_dtype=c_dtype,
      )
  
imports: import itertools
  import functools
  from string import ascii_letters
  from autoray import numpy
  from numpy import float64
  import pennylane
  from .None import single_dispatch
  from .multi_dispatch import diag, dot, scatter_element_add, einsum, get_interface
  from .utils import is_abstract, allclose, cast, convert_like, cast_like
  from .matrix_manipulation import _permute_dense_matrix
label_desc: Use image node_id 7 for calling a global function with example usage: dm_from_state_vector(state, check_state, c_dtype) and returns: reduce_statevector