parameters: (value, dtype=None, dtype_hint=None, name=None)
return_statement: return internal_convert_to_tensor(
        value=value,
        dtype=dtype,
        name=name,
        preferred_dtype=dtype_hint,
        as_ref=False)
docstring: 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`.
      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.
docstring_summary: Converts the given `value` to a `Tensor`.
docstring_tokens: Converts the given value to a Tensor .
function: def convert_to_tensor_v2(value, dtype=None, dtype_hint=None, name=None):
    """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`.
      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 internal_convert_to_tensor(
        value=value,
        dtype=dtype,
        name=name,
        preferred_dtype=dtype_hint,
        as_ref=False)
function_tokens: def convert_to_tensor_v2 ( value , dtype = None , dtype_hint = None , name = None ) : return internal_convert_to_tensor ( value = value , dtype = dtype , name = name , preferred_dtype = dtype_hint , as_ref = False )