code: def bincount(
      arr,
      weights=None,
      minlength=None,
      maxlength=None,
      dtype=dtypes.int32,
      name=None,
      axis=None,
      binary_output=False,
  ):
      """Counts the number of occurrences of each value in an integer array.
  
      If `minlength` and `maxlength` are not given, returns a vector with length
      `tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.
  
      >>> values = tf.constant([1,1,2,3,2,4,4,5])
      >>> tf.math.bincount(values)
      <tf.Tensor: ... numpy=array([0, 2, 2, 1, 2, 1], dtype=int32)>
  
      Vector length = Maximum element in vector `values` is 5. Adding 1, which is 6
                      will be the vector length.
  
      Each bin value in the output indicates number of occurrences of the particular
      index. Here, index 1 in output has a value 2. This indicates value 1 occurs
      two times in `values`.
  
      **Bin-counting with weights**
  
      >>> values = tf.constant([1,1,2,3,2,4,4,5])
      >>> weights = tf.constant([1,5,0,1,0,5,4,5])
      >>> tf.math.bincount(values, weights=weights)
      <tf.Tensor: ... numpy=array([0, 6, 0, 1, 9, 5], dtype=int32)>
  
      When `weights` is specified, bins will be incremented by the corresponding
      weight instead of 1. Here, index 1 in output has a value 6. This is the
      summation of `weights` corresponding to the value in `values` (i.e. for index
      1, the first two values are 1 so the first two weights, 1 and 5, are
      summed).
  
      There is an equivilance between bin-counting with weights and
      `unsorted_segement_sum` where `data` is the weights and `segment_ids` are the
      values.
  
      >>> values = tf.constant([1,1,2,3,2,4,4,5])
      >>> weights = tf.constant([1,5,0,1,0,5,4,5])
      >>> tf.math.unsorted_segment_sum(weights, values, num_segments=6).numpy()
      array([0, 6, 0, 1, 9, 5], dtype=int32)
  
      On GPU, `bincount` with weights is only supported when XLA is enabled
      (typically when a function decorated with `@tf.function(jit_compile=True)`).
      `unsorted_segment_sum` can be used as a workaround for the non-XLA case on
      GPU.
  
      **Bin-counting matrix rows independently**
  
      This example uses `axis=-1` with a 2 dimensional input and returns a
      `Tensor` with bincounting where axis 0 is **not** flattened, i.e. an
      independent bincount for each matrix row.
  
      >>> data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)
      >>> tf.math.bincount(data, axis=-1)
      <tf.Tensor: shape=(2, 4), dtype=int32, numpy=
        array([[1, 1, 1, 1],
               [2, 1, 1, 0]], dtype=int32)>
  
      **Bin-counting with binary_output**
  
      This example gives binary output instead of counting the occurrence.
  
      >>> data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)
      >>> tf.math.bincount(data, axis=-1, binary_output=True)
      <tf.Tensor: shape=(2, 4), dtype=int32, numpy=
        array([[1, 1, 1, 1],
               [1, 1, 1, 0]], dtype=int32)>
  
      **Missing zeros in SparseTensor**
  
      Note that missing zeros (implict zeros) in SparseTensor are **NOT** counted.
      This supports cases such as `0` in the values tensor indicates that index/id
      `0`is present and a missing zero indicates that no index/id is present.
  
      If counting missing zeros is desired, there are workarounds.
      For the `axis=0` case, the number of missing zeros can computed by subtracting
      the number of elements in the SparseTensor's `values` tensor from the
      number of elements in the dense shape, and this difference can be added to the
      first element of the output of `bincount`. For all cases, the SparseTensor
      can be converted to a dense Tensor with `tf.sparse.to_dense` before calling
      `tf.math.bincount`.
  
      Args:
        arr: A Tensor, RaggedTensor, or SparseTensor whose values should be counted.
          These tensors must have a rank of 2 if `axis=-1`.
        weights: If non-None, must be the same shape as arr. For each value in
          `arr`, the bin will be incremented by the corresponding weight instead of
          1. If non-None, `binary_output` must be False.
        minlength: If given, ensures the output has length at least `minlength`,
          padding with zeros at the end if necessary.
        maxlength: If given, skips values in `arr` that are equal or greater than
          `maxlength`, ensuring that the output has length at most `maxlength`.
        dtype: If `weights` is None, determines the type of the output bins.
        name: A name scope for the associated operations (optional).
        axis: The axis to slice over. Axes at and below `axis` will be flattened
          before bin counting. Currently, only `0`, and `-1` are supported. If None,
          all axes will be flattened (identical to passing `0`).
        binary_output: If True, this op will output 1 instead of the number of times
          a token appears (equivalent to one_hot + reduce_any instead of one_hot +
          reduce_add). Defaults to False.
  
      Returns:
        A vector with the same dtype as `weights` or the given `dtype` containing
        the bincount values.
  
      Raises:
        `InvalidArgumentError` if negative values are provided as an input.
  
      """
      name = "bincount" if name is None else name
      with ops.name_scope(name):
          arr = tensor_conversion.convert_to_tensor_v2_with_dispatch(
              arr, name="arr"
          )
          if weights is not None:
              weights = (
                  tensor_conversion.convert_to_tensor_v2_with_dispatch(
                      weights, name="weights"
                  )
              )
  
          if weights is not None and binary_output:
              raise ValueError(
                  "Arguments `binary_output` and `weights` are mutually "
                  "exclusive. Please specify only one."
              )
  
          if not arr.dtype.is_integer:
              arr = math_ops.cast(arr, dtypes.int32)
          if axis is None:
              axis = 0
  
          if axis not in [0, -1]:
              raise ValueError(
                  f"Unsupported value for argument axis={axis}. Only 0 and"
                  " -1 are currently supported."
              )
  
          array_is_nonempty = array_ops.size(arr) > 0
          output_size = math_ops.cast(array_is_nonempty, arr.dtype) * (
              math_ops.reduce_max(arr) + 1
          )
          if minlength is not None:
              minlength = ops.convert_to_tensor(
                  minlength, name="minlength", dtype=arr.dtype
              )
              output_size = gen_math_ops.maximum(minlength, output_size)
          if maxlength is not None:
              maxlength = ops.convert_to_tensor(
                  maxlength, name="maxlength", dtype=arr.dtype
              )
              output_size = gen_math_ops.minimum(maxlength, output_size)
  
          if axis == 0:
              if weights is not None:
                  weights = array_ops.reshape(weights, [-1])
              arr = array_ops.reshape(arr, [-1])
  
          weights = validate_dense_weights(arr, weights, dtype)
          return gen_math_ops.dense_bincount(
              input=arr,
              size=output_size,
              weights=weights,
              binary_output=binary_output,
          )
  
file_path: tensorflow/tensorflow/python/ops/bincount_ops.py
imports: from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling a global function with example usage: bincount(arr, weights, minlength, maxlength, dtype, name, axis, binary_output) and returns: gen_math_ops