code: def __call__(self, a, repeats):
      axis = self._axis
      a = astensor(a)
      if axis is None:
          a = ravel(a)
  
      ax = axis or 0
  
      if not isinstance(repeats, Integral):
          if not isinstance(repeats, Tensor):
              repeats = np.asarray(repeats)
              if repeats.size == 1:
                  repeats = int(repeats[0])
                  size = repeats * a.shape[axis or 0]
              elif a.shape[ax] == 1:
                  size = repeats = int(repeats.sum())
              else:
                  size = int(repeats.sum())
          else:
              size = np.nan
          if not isinstance(repeats, Integral):
              if repeats.ndim != 1:
                  raise ValueError("repeats should be 1-d tensor")
              broadcast_shape(repeats.shape, a.shape[ax : ax + 1])
      else:
          size = a.shape[axis or 0] * repeats
  
      shape = a.shape[:ax] + (size,) + a.shape[ax + 1 :]
      self.dtype = a.dtype
      self.sparse = a.issparse()
  
      inputs = [a]
      if isinstance(repeats, Tensor):
          inputs.append(repeats)
      else:
          self._repeats = repeats
  
      return self.new_tensor(inputs, shape, order=TensorOrder.C_ORDER)
  
imports: import itertools
  from numbers import Integral
  import numpy
  from ...None import opcodes
  from ...core import recursive_tile
  from ...serialization.serializables import KeyField, AnyField, Int32Field
  from ...utils import has_unknown_shape
  from ..core import Tensor, TENSOR_TYPE, TENSOR_CHUNK_TYPE, TensorOrder
  from ..utils import broadcast_shape, unify_chunks
  from ..operands import TensorHasInput, TensorOperandMixin
  from ..datasource import tensor
  from ..array_utils import as_same_device, device
  from .ravel import ravel
label_desc: Use image node_id 5 for calling the TensorRepeat obj's underlying member method code with example usage: obj.__call__(a, repeats) and returns: self