code: def convert_to_tensor_v1_with_dispatch(
      value,
      dtype=None,
      name=None,
      preferred_dtype=None,
      dtype_hint=None,
  ) -> tensor_lib.Tensor:
      """Converts the given `value` to a `Tensor`.
  
      This function converts Python objects of various types to `Tensor`
      objects. It accepts `Tensor` objects, numpy arrays, Python lists,
      and Python scalars. For example:
  
      ```python
      import numpy as np
  
      def my_func(arg):
        arg = tf.convert_to_tensor(arg, dtype=tf.float32)
        return tf.matmul(arg, arg) + arg
  
      # The following calls are equivalent.
      value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))
      value_2 = my_func([[1.0, 2.0], [3.0, 4.0]])
      value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32))
      ```
  
      This function can be useful when composing a new operation in Python
      (such as `my_func` in the example above). All standard Python op
      constructors apply this function to each of their Tensor-valued
      inputs, which allows those ops to accept numpy arrays, Python lists,
      and scalars in addition to `Tensor` objects.
  
      Note: This function diverges from default Numpy behavior for `float` and
        `string` types when `None` is present in a Python list or scalar. Rather
        than silently converting `None` values, an error will be thrown.
  
      Args:
        value: An object whose type has a registered `Tensor` conversion function.
        dtype: Optional element type for the returned tensor. If missing, the type
          is inferred from the type of `value`.
        name: Optional name to use if a new `Tensor` is created.
        preferred_dtype: Optional element type for the returned tensor, used when
          dtype is None. In some cases, a caller may not have a dtype in mind when
          converting to a tensor, so preferred_dtype can be used as a soft
          preference.  If the conversion to `preferred_dtype` is not possible, this
          argument has no effect.
        dtype_hint: same meaning as preferred_dtype, and overrides it.
  
      Returns:
        A `Tensor` based on `value`.
  
      Raises:
        TypeError: If no conversion function is registered for `value` to `dtype`.
        RuntimeError: If a registered conversion function returns an invalid value.
        ValueError: If the `value` is a tensor not of given `dtype` in graph mode.
      """
      return convert_to_tensor_v1(
          value,
          dtype=dtype,
          name=name,
          preferred_dtype=preferred_dtype,
          dtype_hint=dtype_hint,
      )
  
file_path: tensorflow-upstream/tensorflow/python/framework/tensor_conversion.py
imports: from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_conversion_registry
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util import tf_export
label_desc: Use image node_id 2 for calling a global function with example usage: convert_to_tensor_v1_with_dispatch(value, dtype, name, preferred_dtype, dtype_hint) and returns: convert_to_tensor_v1