code: def convert_to_tensor(x, dtype=None, sparse=None):
      """Convert a NumPy array to a tensor.
  
      Args:
          x: A NumPy array.
          dtype: The target type.
          sparse: Whether to keep sparse tensors. `False` will cause sparse
              tensors to be densified. The default value of `None` means that
              sparse tensors are kept only if the backend supports them.
  
      Returns:
          A tensor of the specified `dtype`.
  
      Example:
  
      >>> x = np.array([1, 2, 3])
      >>> y = keras.ops.convert_to_tensor(x)
      """
      return backend.convert_to_tensor(x, dtype=dtype, sparse=sparse)
  
imports: import numpy
  from keras import backend
  from keras.api_export import keras_export
  from keras.backend import KerasTensor
  from keras.backend import any_symbolic_tensors
  from keras.ops.operation import Operation
  from keras.utils import traceback_utils
label_desc: Use image node_id 11 for calling a global function with example usage: convert_to_tensor(x, dtype, sparse) and returns: backend