from tensorflow.python.types import core
from tensorflow.python.util import tf_export

# A representative sample is a map of: input_key -> input_value.
# Ex.: {'dense_input': tf.constant([1, 2, 3])}
# Ex.: {'x1': np.ndarray([4, 5, 6]}
RepresentativeSample = Mapping[str, core.TensorLike]

# A representative dataset is an iterable of representative samples.
RepresentativeDataset = Iterable[RepresentativeSample]

    self._hist_mids = np.linspace(first_mid, last_mid, self._num_bins)

  def _get_dequantized_hist_mids_after_quantize(
      self, quant_min: float, quant_max: float
  ) -> np.ndarray:
    """Quantizes and dequantizes hist_mids using quant_min and quant_max.

    Quantization converts the range of numbers from [quant_min, quant_max] to
    [0, 2^num_bits - 1]. Values less than quant_min are converted to 0, and

func NewArray(elem Type, len int64) *Array { return &Array{len: len, elem: elem} }

// Len returns the length of array a.
// A negative result indicates an unknown length.
func (a *Array) Len() int64 { return a.len }

// Elem returns element type of array a.
func (a *Array) Elem() Type { return a.elem }

func (a *Array) Underlying() Type { return a }

        var first = true
        list.forEach {
            if (first) first = false else comma()
            body(it)
        }
        endArray()
    }

    private
    fun beginArray() {
        write('[')
    }

    private
    fun endArray() {
        write(']')
    }

    private
    fun property(name: String, value: String) {
        property(name) { jsonString(value) }

KaDataFlowExitPointSnapshot:
  defaultExpressionInfo = DefaultExpressionInfo:
    expression = [1, 2, 3]
    type = kotlin.IntArray
  hasEscapingJumps = false
  hasJumps = false
  hasMultipleJumpKinds = false
  hasMultipleJumpTargets = false
  jumpExpressions = []
  returnValueType = null
  valuedReturnExpressions = []

annotation class Anno(val value: IntArray)

@Anno(value = <expr>[1, 2, 3]</expr>)

// Convert updates into canonical form as expected by tf.scatter ops.
//
// tf.scatter expects `update_window_dims` to be the trailing dimensions.
//
// To support scatter ops generated by numpy-like slice updates:
//   nd_array[:, [i,j]] = [i_values, j_values]
//
// `updates` must be transposed when the update_window_dims are the leading
// dimensions of `updates`.
//
// Other values of `update_window_dims` are left unsupported.
//

import org.gradle.internal.serialize.EncoderExtensions.writeLengthPrefixedShorts


object NonPrimitiveArrayCodec : Codec<Array<*>> {
    override suspend fun WriteContext.encode(value: Array<*>) {
        writeArray(value) { element ->
            write(element)
        }
    }

    override suspend fun ReadContext.decode(): Array<*> =
        readArray {
            read()
        }
}

    private List<Object> readArray(JsonReader reader) {
        List<Object> values = []
        reader.beginArray()
        while (reader.peek() != JsonToken.END_ARRAY) {
            Object value = readAny(reader)
            values.add(value)
        }
        reader.endArray()
        return values
    }

    static Map<String, String> normalizeForTests(Map<String, ?> attributes) {

  public final Object[] toArray() {
    return toArray(EMPTY_ARRAY);
  }

  @CanIgnoreReturnValue
  @Override
  /*
   * This suppression is here for two reasons:
   *
   * 1. b/192354773 in our checker affects toArray declarations.
   *
   * 2. `other[size] = null` is unsound. We could "fix" this by requiring callers to pass in an