code: def solve(a, b):
      """Solves a linear matrix equation.
  
      It computes the exact solution of ``x`` in ``ax = b``,
      where ``a`` is a square and full rank matrix.
  
      Args:
          a (cupy.ndarray): The matrix with dimension ``(..., M, M)``.
          b (cupy.ndarray): The matrix with dimension ``(..., M)`` or
              ``(..., M, K)``.
  
      Returns:
          cupy.ndarray:
              The matrix with dimension ``(..., M)`` or ``(..., M, K)``.
  
      .. warning::
          This function calls one or more cuSOLVER routine(s) which may yield
          invalid results if input conditions are not met.
          To detect these invalid results, you can set the `linalg`
          configuration to a value that is not `ignore` in
          :func:`cupyx.errstate` or :func:`cupyx.seterr`.
  
      .. seealso:: :func:`numpy.linalg.solve`
      """
      from cupyx import lapack
      from cupy.cublas import batched_gesv, get_batched_gesv_limit
  
      if a.ndim > 2 and a.shape[-1] <= get_batched_gesv_limit():
          # Note: There is a low performance issue in batched_gesv when matrix is
          # large, so it is not used in such cases.
          return batched_gesv(a, b)
  
      # TODO(kataoka): Move the checks to the beginning
      _util._assert_cupy_array(a, b)
      _util._assert_stacked_2d(a)
      _util._assert_stacked_square(a)
  
      # TODO(kataoka): Support broadcast
      if not (
          (a.ndim == b.ndim or a.ndim == b.ndim + 1)
          and a.shape[:-1] == b.shape[: a.ndim - 1]
      ):
          raise ValueError(
              "a must have (..., M, M) shape and b must have (..., M) "
              "or (..., M, K)"
          )
  
      dtype, out_dtype = _util.linalg_common_type(a, b)
      if b.size == 0:
          return cupy.empty(b.shape, out_dtype)
  
      if a.ndim == 2:
          # prevent 'a' and 'b' to be overwritten
          a = a.astype(dtype, copy=True, order="F")
          b = b.astype(dtype, copy=True, order="F")
          lapack.gesv(a, b)
          return b.astype(out_dtype, copy=False)
  
      # prevent 'a' to be overwritten
      a = a.astype(dtype, copy=True, order="C")
      x = cupy.empty_like(b, dtype=out_dtype)
      shape = a.shape[:-2]
      for i in range(numpy.prod(shape)):
          index = numpy.unravel_index(i, shape)
          # prevent 'b' to be overwritten
          bi = b[index].astype(dtype, copy=True, order="F")
          lapack.gesv(a[index], bi)
          x[index] = bi
      return x
  
file_path: cupy/cupy/linalg/_solve.py
imports: import warnings
  import numpy
  from numpy import linalg
  import cupy
  from cupy._core import internal
  from cupy.cuda import device
  from cupy.linalg import _decomposition
  from cupy.linalg import _util
  import cupyx
label_desc: Use image node_id 1 for calling a global function with example usage: solve(a, b) and returns: x, batched_gesv, cupy, b