code: def __call__(self, t, dtype=None, shape=None):
      if dtype is None:
          try:
              kwargs = self.kwargs or dict()
              if self.with_chunk_index:
                  kwargs["chunk_index"] = (0,) * t.ndim
              with np.errstate(all="ignore"), quiet_stdio():
                  mock_result = self.func(
                      np.random.rand(2, 2).astype(t.dtype),
                      *(self.args or ()),
                      **kwargs,
                  )
          except:
              raise TypeError(
                  "Cannot estimate output type of map_chunk call"
              )
          dtype = mock_result.dtype
  
      if shape is not None:
          new_shape = shape
      else:
          new_shape = (
              t.shape if self.elementwise else (np.nan,) * t.ndim
          )
      inputs = (
          [t]
          + find_objects(self.args, ENTITY_TYPE)
          + find_objects(self.kwargs, ENTITY_TYPE)
      )
      return self.new_tensor(inputs, dtype=dtype, shape=new_shape)
  
imports: import numpy
  from ...None import opcodes
  from ...core import ENTITY_TYPE, CHUNK_TYPE, recursive_tile
  from ...core.custom_log import redirect_custom_log
  from ...serialization.serializables import FunctionField, BoolField, TupleField, DictField
  from ...utils import enter_current_session, quiet_stdio, find_objects, replace_objects, has_unknown_shape
  from ..operands import TensorOperand, TensorOperandMixin
label_desc: Use image node_id 8 for calling the TensorMapChunk obj's underlying member method code with example usage: obj.__call__(t, dtype, shape) and returns: self