@tf_export(v1=['math.bincount', 'bincount'])
@deprecation.deprecated_endpoints('bincount')
def bincount_v1(arr, weights=None, minlength=None, maxlength=None, dtype=dtypes.int32):
    'Counts the number of occurrences of each value in an integer array.\n\n  If `minlength` and `maxlength` are not given, returns a vector with length\n  `tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.\n  If `weights` are non-None, then index `i` of the output stores the sum of the\n  value in `weights` at each index where the corresponding value in `arr` is\n  `i`.\n\n  Args:\n    arr: An int32 tensor of non-negative values.\n    weights: If non-None, must be the same shape as arr. For each value in\n      `arr`, the bin will be incremented by the corresponding weight instead of\n      1.\n    minlength: If given, ensures the output has length at least `minlength`,\n      padding with zeros at the end if necessary.\n    maxlength: If given, skips values in `arr` that are equal or greater than\n      `maxlength`, ensuring that the output has length at most `maxlength`.\n    dtype: If `weights` is None, determines the type of the output bins.\n\n  Returns:\n    A vector with the same dtype as `weights` or the given `dtype`. The bin\n    values.\n  '
    return bincount(arr, weights, minlength, maxlength, dtype)
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.
If `weights` are non-None, then index `i` of the output stores the sum of the
value in `weights` at each index where the corresponding value in `arr` is
`i`.

Args:
  arr: An int32 tensor of non-negative values.
  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.
  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.

Returns:
  A vector with the same dtype as `weights` or the given `dtype`. The bin
  values.
@tf_export(v1=['math.bincount', 'bincount'])
@deprecation.deprecated_endpoints('bincount')
def bincount_v1(arr, weights=None, minlength=None, maxlength=None, dtype=dtypes.int32):
    'Counts the number of occurrences of each value in an integer array.\n\n  If `minlength` and `maxlength` are not given, returns a vector with length\n  `tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.\n  If `weights` are non-None, then index `i` of the output stores the sum of the\n  value in `weights` at each index where the corresponding value in `arr` is\n  `i`.\n\n  Args:\n    arr: An int32 tensor of non-negative values.\n    weights: If non-None, must be the same shape as arr. For each value in\n      `arr`, the bin will be incremented by the corresponding weight instead of\n      1.\n    minlength: If given, ensures the output has length at least `minlength`,\n      padding with zeros at the end if necessary.\n    maxlength: If given, skips values in `arr` that are equal or greater than\n      `maxlength`, ensuring that the output has length at most `maxlength`.\n    dtype: If `weights` is None, determines the type of the output bins.\n\n  Returns:\n    A vector with the same dtype as `weights` or the given `dtype`. The bin\n    values.\n  '
    return bincount(arr, weights, minlength, maxlength, dtype)