class_docstr: Implements the Numpy backend ops for a PC auto-batching VM.
class_docstr_tok: ["Implements","the","Numpy","backend","ops","for","a","PC","auto-batching","VM","."]
docstr: Returns the `instructions.Type` of `t`.
  
  Args:
    t: `np.ndarray` or a Python constant.
    dtype_hint: dtype to prefer, if `t` is a constant.
  
  Returns:
    vm_type: `instructions.TensorType` describing `t`
docstr_tok: ["Returns","the","`","instructions.Type","`","of","`","t","`",".","Args",":","t",":","`","np.ndarray","`","or","a","Python","constant",".","dtype_hint",":","dtype","to","prefer",",","if","`","t","`","is","a","constant",".","Returns",":","vm_type",":","`","instructions.TensorType","`","describing","`","t","`"]
code: def type_of(self, t, dtype_hint=None):
      """Returns the `instructions.Type` of `t`.
  
      Args:
        t: `np.ndarray` or a Python constant.
        dtype_hint: dtype to prefer, if `t` is a constant.
  
      Returns:
        vm_type: `instructions.TensorType` describing `t`
      """
      t = np.array(t, dtype=dtype_hint)
      return instructions.TensorType(t.dtype, t.shape)
  
imports: import collections
  import numpy
  from tensorflow_probability.python.experimental.auto_batching import instructions
label_desc: Use image node_id 2 for calling the NumpyBackend obj's underlying member method code with example usage: obj.type_of(t, dtype_hint) and returns: instructions