code: def convert_to_tensor_v2_with_dispatch(
      value, dtype=None, dtype_hint=None, name=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:
  
      >>> import numpy as np
      >>> def my_func(arg):
      ...   arg = tf.convert_to_tensor(arg, dtype=tf.float32)
      ...   return arg
  
      >>> # The following calls are equivalent.
      ...
      >>> value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))
      >>> print(value_1)
      tf.Tensor(
        [[1. 2.]
         [3. 4.]], shape=(2, 2), dtype=float32)
      >>> value_2 = my_func([[1.0, 2.0], [3.0, 4.0]])
      >>> print(value_2)
      tf.Tensor(
        [[1. 2.]
         [3. 4.]], shape=(2, 2), dtype=float32)
      >>> value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32))
      >>> print(value_3)
      tf.Tensor(
        [[1. 2.]
         [3. 4.]], shape=(2, 2), dtype=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`.
        dtype_hint: 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 dtype_hint can be used as a soft preference. If
          the conversion to `dtype_hint` is not possible, this argument has no
          effect.
        name: Optional name to use if a new `Tensor` is created.
  
      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_v2(
          value, dtype=dtype, dtype_hint=dtype_hint, name=name
      )
  
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 3 for calling a global function with example usage: convert_to_tensor_v2_with_dispatch(value, dtype, dtype_hint, name) and returns: convert_to_tensor_v2